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Bibliography on: Microbiome Project(s)

Robert J. Robbins is a biologist, an educator, a science administrator, a
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21 Nov 2018 at 01:40Created:

Microbiome Project(s)

For many multicellular organisms, a microscopic study shows that microbial cells
outnumber host cells by perhaps ten to one. Until recently, these abundant
communities of host-associated microbes were largely unstudied, often for lack
of analytical tools or conceptual frameworks. The advent of new tools is rendering
visible this previously ignored biosphere and the results have been startling.
Many facets of host biology have proven to be profoundly affected by the
associated microbiomes. As a result, several large-scale projects — such as
the
Human Microbiome Project
— have been undertaken to jump start an understanding
of this critical component of the biosphere.

Created with PubMed® Query:
"microbiome project" NOT pmcbook NOT ispreviousversion

Citations
The Papers
(from PubMed®)

RevDate: 2018-11-19

Rajakovich LJ, EP Balskus (2018)

Metabolic functions of the human gut microbiota: the role of metalloenzymes.

Natural product reports [Epub ahead of print].

Covering: up to the end of 2017The human body is composed of an equal number of human and microbial cells. While the microbial community inhabiting the human gastrointestinal tract plays an essential role in host health, these organisms have also been connected to various diseases. Yet, the gut microbial functions that modulate host biology are not well established. In this review, we describe metabolic functions of the human gut microbiota that involve metalloenzymes. These activities enable gut microbial colonization, mediate interactions with the host, and impact human health and disease. We highlight cases in which enzyme characterization has advanced our understanding of the gut microbiota and examples that illustrate the diverse ways in which metalloenzymes facilitate both essential and unique functions of this community. Finally, we analyze Human Microbiome Project sequencing datasets to assess the distribution of a prominent family of metalloenzymes in human-associated microbial communities, guiding future enzyme characterization efforts.

@article {pmid30452039,
year = {2018},
author = {Rajakovich, LJ and Balskus, EP},
title = {Metabolic functions of the human gut microbiota: the role of metalloenzymes.},
journal = {Natural product reports},
volume = {},
number = {},
pages = {},
doi = {10.1039/c8np00074c},
pmid = {30452039},
issn = {1460-4752},
abstract = {Covering: up to the end of 2017The human body is composed of an equal number of human and microbial cells. While the microbial community inhabiting the human gastrointestinal tract plays an essential role in host health, these organisms have also been connected to various diseases. Yet, the gut microbial functions that modulate host biology are not well established. In this review, we describe metabolic functions of the human gut microbiota that involve metalloenzymes. These activities enable gut microbial colonization, mediate interactions with the host, and impact human health and disease. We highlight cases in which enzyme characterization has advanced our understanding of the gut microbiota and examples that illustrate the diverse ways in which metalloenzymes facilitate both essential and unique functions of this community. Finally, we analyze Human Microbiome Project sequencing datasets to assess the distribution of a prominent family of metalloenzymes in human-associated microbial communities, guiding future enzyme characterization efforts.},
}

RevDate: 2018-11-14

Su X, Jing G, McDonald D, et al (2018)

Identifying and Predicting Novelty in Microbiome Studies.

mBio, 9(6): pii:mBio.02099-18.

With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated reference microbiome database organized by taxonomic similarity at the whole-microbiome level. Tracking the microbiome novelty score (MNS) over 8 years of microbiome depositions based on searching in more than 100,000 global 16S rRNA gene amplicon samples, we detected that the structural novelty of human microbiomes is approaching saturation and likely bounded, whereas that in environmental habitats remains 5 times higher. Via the microbiome focus index (MFI), which is derived from the MNS and microbiome attention score (MAS), we objectively track and compare the structural-novelty and attracted-attention scores of individual microbiome samples and projects, and we predict future trends in the field. For example, marine and indoor environments and mother-baby interactions are likely to receive disproportionate additional attention based on recent trends. Therefore, MNS, MAS, and MFI are proposed "alt-metrics" for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.IMPORTANCE We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as "alt-metrics" for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.

@article {pmid30425147,
year = {2018},
author = {Su, X and Jing, G and McDonald, D and Wang, H and Wang, Z and Gonzalez, A and Sun, Z and Huang, S and Navas, J and Knight, R and Xu, J},
title = {Identifying and Predicting Novelty in Microbiome Studies.},
journal = {mBio},
volume = {9},
number = {6},
pages = {},
doi = {10.1128/mBio.02099-18},
pmid = {30425147},
issn = {2150-7511},
abstract = {With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated reference microbiome database organized by taxonomic similarity at the whole-microbiome level. Tracking the microbiome novelty score (MNS) over 8 years of microbiome depositions based on searching in more than 100,000 global 16S rRNA gene amplicon samples, we detected that the structural novelty of human microbiomes is approaching saturation and likely bounded, whereas that in environmental habitats remains 5 times higher. Via the microbiome focus index (MFI), which is derived from the MNS and microbiome attention score (MAS), we objectively track and compare the structural-novelty and attracted-attention scores of individual microbiome samples and projects, and we predict future trends in the field. For example, marine and indoor environments and mother-baby interactions are likely to receive disproportionate additional attention based on recent trends. Therefore, MNS, MAS, and MFI are proposed "alt-metrics" for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.IMPORTANCE We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as "alt-metrics" for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.},
}

RevDate: 2018-11-13

Frioux C, Fremy E, Trottier C, et al (2018)

Scalable and exhaustive screening of metabolic functions carried out by microbial consortia.

Bioinformatics (Oxford, England), 34(17):i934-i943.

Motivation: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.

Results: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.

Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.

@article {pmid30423063,
year = {2018},
author = {Frioux, C and Fremy, E and Trottier, C and Siegel, A},
title = {Scalable and exhaustive screening of metabolic functions carried out by microbial consortia.},
journal = {Bioinformatics (Oxford, England)},
volume = {34},
number = {17},
pages = {i934-i943},
doi = {10.1093/bioinformatics/bty588},
pmid = {30423063},
issn = {1367-4811},
abstract = {Motivation: The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.

Results: We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.

Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.},
}

According to the Human Microbiome Project (HMP), a healthy human body contains ten times more microbes than human cells. Microbial communities colonize different organs of the body, playing fundamental roles both in human health and disease. Despite the vast scientific knowledge of the role of microbial communities in a living body, little is known at present about microbial changes occurring after death, thus leading many authors to investigate the composition of the thanatomicrobiome and its potential applications in the forensic field. The aim of the following review is to provide a general overview of the advances of postmortem microbiology research, mainly focusing on the role of microbiological investigations carried out on internal organs and fluids. To this end, a total of 19 studies have been sistematically reviewed, each one chosen according to specific inclusion/exclusion criteria. The selected studies assess the contribution of contamination, postmortem transmigration and agonal spread to microbial isolation from dead body samples, and shed light on the role of postmortem microbiological investigations in several forensic fields, such as cause of death or PMI determination.

@article {pmid30419494,
year = {2018},
author = {Ventura Spagnolo, E and Stassi, C and Mondello, C and Zerbo, S and Milone, L and Argo, A},
title = {Forensic microbiology applications: A systematic review.},
journal = {Legal medicine (Tokyo, Japan)},
volume = {36},
number = {},
pages = {73-80},
doi = {10.1016/j.legalmed.2018.11.002},
pmid = {30419494},
issn = {1873-4162},
abstract = {According to the Human Microbiome Project (HMP), a healthy human body contains ten times more microbes than human cells. Microbial communities colonize different organs of the body, playing fundamental roles both in human health and disease. Despite the vast scientific knowledge of the role of microbial communities in a living body, little is known at present about microbial changes occurring after death, thus leading many authors to investigate the composition of the thanatomicrobiome and its potential applications in the forensic field. The aim of the following review is to provide a general overview of the advances of postmortem microbiology research, mainly focusing on the role of microbiological investigations carried out on internal organs and fluids. To this end, a total of 19 studies have been sistematically reviewed, each one chosen according to specific inclusion/exclusion criteria. The selected studies assess the contribution of contamination, postmortem transmigration and agonal spread to microbial isolation from dead body samples, and shed light on the role of postmortem microbiological investigations in several forensic fields, such as cause of death or PMI determination.},
}

RevDate: 2018-11-07

Ojo-Okunola A, Nicol M, E du Toit (2018)

Human Breast Milk Bacteriome in Health and Disease.

Nutrients, 10(11): pii:nu10111643.

It is well-known that, beyond nutritional components, human breast milk (HBM) contains a wide variety of non-nutritive bio-factors perfectly suited for the growing infant. In the pre-2000 era, HBM was considered sterile and devoid of micro-organisms. Though HBM was not included as part of the human microbiome project launched in 2007, great strides have been made in studying the bacterial diversity of HBM in both a healthy state and diseased state, and in understanding their role in infant health. HBM provides a vast array of beneficial micro-organisms that play a key role in colonizing the infant's mucosal system, including that of the gut. They also have a role in priming the infant's immune system and supporting its maturation. In this review, we provide an in-depth and updated insight into the immunomodulatory, metabolic, and anti-infective role of HBM bacteriome (bacterial community) and its effect on infant health. We also provide key information from the literature by exploring the possible origin of microbial communities in HBM, the bacterial diversity in this niche and the determinants influencing the HBM bacteriome. Lastly, we investigate the role of the HBM bacteriome in maternal infectious disease (human immunodeficiency virus (HIV) and mastitis)), and cancer. Key gaps in HBM bacterial research are also identified.

@article {pmid30400268,
year = {2018},
author = {Ojo-Okunola, A and Nicol, M and du Toit, E},
title = {Human Breast Milk Bacteriome in Health and Disease.},
journal = {Nutrients},
volume = {10},
number = {11},
pages = {},
doi = {10.3390/nu10111643},
pmid = {30400268},
issn = {2072-6643},
support = {U54HG009824//NIH Office of the Director/ ; 1U01AI110466-01A1//H3Africa U01 AWARD FROM THE NATIONAL INSTITUTES OF HEALTH OF THE USA/ ; OPP1017641; OPP1017579//Bill and Melinda Gates Foundation/ ; },
abstract = {It is well-known that, beyond nutritional components, human breast milk (HBM) contains a wide variety of non-nutritive bio-factors perfectly suited for the growing infant. In the pre-2000 era, HBM was considered sterile and devoid of micro-organisms. Though HBM was not included as part of the human microbiome project launched in 2007, great strides have been made in studying the bacterial diversity of HBM in both a healthy state and diseased state, and in understanding their role in infant health. HBM provides a vast array of beneficial micro-organisms that play a key role in colonizing the infant's mucosal system, including that of the gut. They also have a role in priming the infant's immune system and supporting its maturation. In this review, we provide an in-depth and updated insight into the immunomodulatory, metabolic, and anti-infective role of HBM bacteriome (bacterial community) and its effect on infant health. We also provide key information from the literature by exploring the possible origin of microbial communities in HBM, the bacterial diversity in this niche and the determinants influencing the HBM bacteriome. Lastly, we investigate the role of the HBM bacteriome in maternal infectious disease (human immunodeficiency virus (HIV) and mastitis)), and cancer. Key gaps in HBM bacterial research are also identified.},
}

I extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity-area relationship). The extension was aimed to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling-species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha-diversity and multiplicative beta-diversity. In particular, Hill numbers are essentially a series of Renyi's entropy values weighted differently along the rare-common-dominant spectrum of species abundance distribution and are in the units of effective number of species (or species equivalents such as OTUs). I therefore postulate that Hill numbers should follow the same or similar law of the traditional SAR. I test the postulation with the American gut microbiome project (AGP) dataset of 1,473 healthy North American individuals. I further propose three new concepts and develop their statistical estimation formulae based on the new DAR extension, including: (i) DAR profile-z-q relationship (DAR scaling parameter z at different diversity order q), (ii) PDO (pair-wise diversity overlap) profile-g-q relationship (PDO parameter g at order q, and (iii) MAD (maximal accrual diversity: Dmax) profile-Dmax-q. While the classic SAR is a special case of our new DAR profile, the PDO and MAD profiles offer novel tools for analyzing biodiversity (including alpha-diversity and beta-diversity) and biogeography over space.

@article {pmid30397444,
year = {2018},
author = {Ma, ZS},
title = {DAR (diversity-area relationship): Extending classic SAR (species-area relationship) for biodiversity and biogeography analyses.},
journal = {Ecology and evolution},
volume = {8},
number = {20},
pages = {10023-10038},
doi = {10.1002/ece3.4425},
pmid = {30397444},
issn = {2045-7758},
abstract = {I extend the classic SAR, which has achieved status of ecological law and plays a critical role in global biodiversity and biogeography analyses, to general DAR (diversity-area relationship). The extension was aimed to remedy a serious application limitation of the traditional SAR that only addressed one aspect of biodiversity scaling-species richness scaling over space, but ignoring species abundance information. The extension was further inspired by a recent consensus that Hill numbers offer the most appropriate measures for alpha-diversity and multiplicative beta-diversity. In particular, Hill numbers are essentially a series of Renyi's entropy values weighted differently along the rare-common-dominant spectrum of species abundance distribution and are in the units of effective number of species (or species equivalents such as OTUs). I therefore postulate that Hill numbers should follow the same or similar law of the traditional SAR. I test the postulation with the American gut microbiome project (AGP) dataset of 1,473 healthy North American individuals. I further propose three new concepts and develop their statistical estimation formulae based on the new DAR extension, including: (i) DAR profile-z-q relationship (DAR scaling parameter z at different diversity order q), (ii) PDO (pair-wise diversity overlap) profile-g-q relationship (PDO parameter g at order q, and (iii) MAD (maximal accrual diversity: Dmax) profile-Dmax-q. While the classic SAR is a special case of our new DAR profile, the PDO and MAD profiles offer novel tools for analyzing biodiversity (including alpha-diversity and beta-diversity) and biogeography over space.},
}

BACKGROUND: Human Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However, the identification of microbe-noninfectious disease associations (MDAs) is still a challenging task, because of both the high cost and the limitation of microbe cultivation. Thus, there is a need to develop fast approaches to screen potential MDAs. The growing number of validated MDAs enables us to meet the demand in a new insight. Computational approaches, especially machine learning, are promising to predict MDA candidates rapidly among a large number of microbe-disease pairs with the advantage of no limitation on microbe cultivation. Nevertheless, a few computational efforts at predicting MDAs are made so far.

RESULTS: In this paper, grouping a set of MDAs into a binary MDA matrix, we propose a novel predictive approach (BMCMDA) based on Binary Matrix Completion to predict potential MDAs. The proposed BMCMDA assumes that the incomplete observed MDA matrix is the summation of a latent parameterizing matrix and a noising matrix. It also assumes that the independently occurring subscripts of observed entries in the MDA matrix follows a binomial model. Adopting a standard mean-zero Gaussian distribution for the nosing matrix, we model the relationship between the parameterizing matrix and the MDA matrix under the observed microbe-disease pairs as a probit regression. With the recovered parameterizing matrix, BMCMDA deduces how likely a microbe would be associated with a particular disease. In the experiment under leave-one-out cross-validation, it exhibits the inspiring performance (AUC = 0.906, AUPR =0.526) and demonstrates its superiority by ~ 7% and ~ 5% improvements in terms of AUC and AUPR respectively in the comparison with the pioneering approach KATZHMDA.

CONCLUSIONS: Our BMCMDA provides an effective approach for predicting MDAs and can be also extended to other similar predicting tasks of binary relationship (e.g. protein-protein interaction, drug-target interaction).

@article {pmid30367598,
year = {2018},
author = {Shi, JY and Huang, H and Zhang, YN and Cao, JB and Yiu, SM},
title = {BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completion.},
journal = {BMC bioinformatics},
volume = {19},
number = {Suppl 9},
pages = {281},
doi = {10.1186/s12859-018-2274-3},
pmid = {30367598},
issn = {1471-2105},
abstract = {BACKGROUND: Human Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However, the identification of microbe-noninfectious disease associations (MDAs) is still a challenging task, because of both the high cost and the limitation of microbe cultivation. Thus, there is a need to develop fast approaches to screen potential MDAs. The growing number of validated MDAs enables us to meet the demand in a new insight. Computational approaches, especially machine learning, are promising to predict MDA candidates rapidly among a large number of microbe-disease pairs with the advantage of no limitation on microbe cultivation. Nevertheless, a few computational efforts at predicting MDAs are made so far.

RESULTS: In this paper, grouping a set of MDAs into a binary MDA matrix, we propose a novel predictive approach (BMCMDA) based on Binary Matrix Completion to predict potential MDAs. The proposed BMCMDA assumes that the incomplete observed MDA matrix is the summation of a latent parameterizing matrix and a noising matrix. It also assumes that the independently occurring subscripts of observed entries in the MDA matrix follows a binomial model. Adopting a standard mean-zero Gaussian distribution for the nosing matrix, we model the relationship between the parameterizing matrix and the MDA matrix under the observed microbe-disease pairs as a probit regression. With the recovered parameterizing matrix, BMCMDA deduces how likely a microbe would be associated with a particular disease. In the experiment under leave-one-out cross-validation, it exhibits the inspiring performance (AUC = 0.906, AUPR =0.526) and demonstrates its superiority by ~ 7% and ~ 5% improvements in terms of AUC and AUPR respectively in the comparison with the pioneering approach KATZHMDA.

CONCLUSIONS: Our BMCMDA provides an effective approach for predicting MDAs and can be also extended to other similar predicting tasks of binary relationship (e.g. protein-protein interaction, drug-target interaction).},
}

RevDate: 2018-10-23

Chaudhari NM, Gautam A, Gupta VK, et al (2018)

PanGFR-HM: A Dynamic Web Resource for Pan-Genomic and Functional Profiling of Human Microbiome With Comparative Features.

Frontiers in microbiology, 9:2322.

The conglomerate of microorganisms inhabiting various body-sites of human, known as the human microbiome, is one of the key determinants of human health and disease. Comprehensive pan-genomic and functional analysis approach for human microbiome components can enrich our understanding about impact of microbiome on human health. By utilizing this approach we developed PanGFR-HM (http://www.bioinfo.iicb.res.in/pangfr-hm/) - a novel dynamic web-resource that integrates genomic and functional characteristics of 1293 complete microbial genomes available from Human Microbiome Project. The resource allows users to explore genomic/functional diversity and genome-based phylogenetic relationships between human associated microbial genomes, not provided by any other resource. The key features implemented here include pan-genome and functional analysis of organisms based on taxonomy or body-site, and comparative analysis between groups of organisms. The first feature can also identify probable gene-loss events and significantly over/under represented KEGG/COG categories within pan-genome. The unique second feature can perform comparative genomic, functional and pathways analysis between 4 groups of microbes. The dynamic nature of this resource enables users to define parameters for orthologous clustering and to select any set of organisms for analysis. As an application for comparative feature of PanGFR-HM, we performed a comparative analysis with 67 Lactobacillus genomes isolated from human gut, oral cavity and urogenital tract, and therefore characterized the body-site specific genes, enzymes and pathways. Altogether, PanGFR-HM, being unique in its content and functionality, is expected to provide a platform for microbiome-based comparative functional and evolutionary genomics.

@article {pmid30349509,
year = {2018},
author = {Chaudhari, NM and Gautam, A and Gupta, VK and Kaur, G and Dutta, C and Paul, S},
title = {PanGFR-HM: A Dynamic Web Resource for Pan-Genomic and Functional Profiling of Human Microbiome With Comparative Features.},
journal = {Frontiers in microbiology},
volume = {9},
number = {},
pages = {2322},
doi = {10.3389/fmicb.2018.02322},
pmid = {30349509},
issn = {1664-302X},
abstract = {The conglomerate of microorganisms inhabiting various body-sites of human, known as the human microbiome, is one of the key determinants of human health and disease. Comprehensive pan-genomic and functional analysis approach for human microbiome components can enrich our understanding about impact of microbiome on human health. By utilizing this approach we developed PanGFR-HM (http://www.bioinfo.iicb.res.in/pangfr-hm/) - a novel dynamic web-resource that integrates genomic and functional characteristics of 1293 complete microbial genomes available from Human Microbiome Project. The resource allows users to explore genomic/functional diversity and genome-based phylogenetic relationships between human associated microbial genomes, not provided by any other resource. The key features implemented here include pan-genome and functional analysis of organisms based on taxonomy or body-site, and comparative analysis between groups of organisms. The first feature can also identify probable gene-loss events and significantly over/under represented KEGG/COG categories within pan-genome. The unique second feature can perform comparative genomic, functional and pathways analysis between 4 groups of microbes. The dynamic nature of this resource enables users to define parameters for orthologous clustering and to select any set of organisms for analysis. As an application for comparative feature of PanGFR-HM, we performed a comparative analysis with 67 Lactobacillus genomes isolated from human gut, oral cavity and urogenital tract, and therefore characterized the body-site specific genes, enzymes and pathways. Altogether, PanGFR-HM, being unique in its content and functionality, is expected to provide a platform for microbiome-based comparative functional and evolutionary genomics.},
}

RevDate: 2018-10-23

Chen Z, Yeoh YK, Hui M, et al (2018)

Diversity of macaque microbiota compared to the human counterparts.

Scientific reports, 8(1):15573 pii:10.1038/s41598-018-33950-6.

Studies on the microbial communities in non-human primate hosts provide unique insights in both evolution and function of microbes related to human health and diseases. Using 16S rRNA gene amplicon profiling, we examined the oral, anal and vaginal microbiota in a group of non-captive rhesus macaques (N = 116) and compared the compositions with the healthy communities from Human Microbiome Project. The macaque microbiota was dominated by Bacteroidetes, Firmicutes and Proteobacteria; however, there were marked differences in phylotypes enriched across body sites indicative of strong niche specialization. Compared to human gut microbiota where Bacteroides predominately enriched, the surveyed macaque anal community exhibited increased abundance of Prevotella. In contrast to the conserved human vaginal microbiota extremely dominated by Lactobacillus, the macaque vaginal microbial composition was highly diverse while lactobacilli were rare. A constant decrease of the vaginal microbiota diversity was observed among macaque samples from juvenile, adult without tubectomy, and adult with tubectomy, with the most notable distinction being the enrichment of Halomonas in juvenile and Saccharofermentans in contracepted adults. Both macaque and human oral microbiota were colonized with three most common oral bacterial genera: Streptococcus, Haemophilus and Veillonella, and shared relatively conserved communities to each other. A number of bacteria related to human pathogens were consistently detected in macaques. The findings delineate the range of structure and diversity of microbial communities in a wild macaque population, and enable the application of macaque as an animal model for future characterization of microbes in transmission, genomics and function.

@article {pmid30349024,
year = {2018},
author = {Chen, Z and Yeoh, YK and Hui, M and Wong, PY and Chan, MCW and Ip, M and Yu, J and Burk, RD and Chan, FKL and Chan, PKS},
title = {Diversity of macaque microbiota compared to the human counterparts.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {15573},
doi = {10.1038/s41598-018-33950-6},
pmid = {30349024},
issn = {2045-2322},
abstract = {Studies on the microbial communities in non-human primate hosts provide unique insights in both evolution and function of microbes related to human health and diseases. Using 16S rRNA gene amplicon profiling, we examined the oral, anal and vaginal microbiota in a group of non-captive rhesus macaques (N = 116) and compared the compositions with the healthy communities from Human Microbiome Project. The macaque microbiota was dominated by Bacteroidetes, Firmicutes and Proteobacteria; however, there were marked differences in phylotypes enriched across body sites indicative of strong niche specialization. Compared to human gut microbiota where Bacteroides predominately enriched, the surveyed macaque anal community exhibited increased abundance of Prevotella. In contrast to the conserved human vaginal microbiota extremely dominated by Lactobacillus, the macaque vaginal microbial composition was highly diverse while lactobacilli were rare. A constant decrease of the vaginal microbiota diversity was observed among macaque samples from juvenile, adult without tubectomy, and adult with tubectomy, with the most notable distinction being the enrichment of Halomonas in juvenile and Saccharofermentans in contracepted adults. Both macaque and human oral microbiota were colonized with three most common oral bacterial genera: Streptococcus, Haemophilus and Veillonella, and shared relatively conserved communities to each other. A number of bacteria related to human pathogens were consistently detected in macaques. The findings delineate the range of structure and diversity of microbial communities in a wild macaque population, and enable the application of macaque as an animal model for future characterization of microbes in transmission, genomics and function.},
}

RevDate: 2018-10-17

Wyman SK, Avila-Herrera A, Nayfach S, et al (2018)

A most wanted list of conserved microbial protein families with no known domains.

PloS one, 13(10):e0205749 pii:PONE-D-18-08043.

The number and proportion of genes with no known function are growing rapidly. To quantify this phenomenon and provide criteria for prioritizing genes for functional characterization, we developed a bioinformatics pipeline that identifies robustly defined protein families with no annotated domains, ranks these with respect to phylogenetic breadth, and identifies them in metagenomics data. We applied this approach to 271 965 protein families from the SFams database and discovered many with no functional annotation, including >118 000 families lacking any known protein domain. From these, we prioritized 6 668 conserved protein families with at least three sequences from organisms in at least two distinct classes. These Function Unknown Families (FUnkFams) are present in Tara Oceans Expedition and Human Microbiome Project metagenomes, with distributions associated with sampling environment. Our findings highlight the extent of functional novelty in sequence databases and establish an approach for creating a "most wanted" list of genes to prioritize for further characterization.

@article {pmid30332487,
year = {2018},
author = {Wyman, SK and Avila-Herrera, A and Nayfach, S and Pollard, KS},
title = {A most wanted list of conserved microbial protein families with no known domains.},
journal = {PloS one},
volume = {13},
number = {10},
pages = {e0205749},
doi = {10.1371/journal.pone.0205749},
pmid = {30332487},
issn = {1932-6203},
abstract = {The number and proportion of genes with no known function are growing rapidly. To quantify this phenomenon and provide criteria for prioritizing genes for functional characterization, we developed a bioinformatics pipeline that identifies robustly defined protein families with no annotated domains, ranks these with respect to phylogenetic breadth, and identifies them in metagenomics data. We applied this approach to 271 965 protein families from the SFams database and discovered many with no functional annotation, including >118 000 families lacking any known protein domain. From these, we prioritized 6 668 conserved protein families with at least three sequences from organisms in at least two distinct classes. These Function Unknown Families (FUnkFams) are present in Tara Oceans Expedition and Human Microbiome Project metagenomes, with distributions associated with sampling environment. Our findings highlight the extent of functional novelty in sequence databases and establish an approach for creating a "most wanted" list of genes to prioritize for further characterization.},
}

RevDate: 2018-10-02

Gonzalez A, Navas-Molina JA, Kosciolek T, et al (2018)

Qiita: rapid, web-enabled microbiome meta-analysis.

Nature methods, 15(10):796-798.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

@article {pmid30275573,
year = {2018},
author = {Gonzalez, A and Navas-Molina, JA and Kosciolek, T and McDonald, D and Vázquez-Baeza, Y and Ackermann, G and DeReus, J and Janssen, S and Swafford, AD and Orchanian, SB and Sanders, JG and Shorenstein, J and Holste, H and Petrus, S and Robbins-Pianka, A and Brislawn, CJ and Wang, M and Rideout, JR and Bolyen, E and Dillon, M and Caporaso, JG and Dorrestein, PC and Knight, R},
title = {Qiita: rapid, web-enabled microbiome meta-analysis.},
journal = {Nature methods},
volume = {15},
number = {10},
pages = {796-798},
doi = {10.1038/s41592-018-0141-9},
pmid = {30275573},
issn = {1548-7105},
abstract = {Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.},
}

RevDate: 2018-10-16

Aziz RK, Hegazy SM, Yasser R, et al (2018)

Drug pharmacomicrobiomics and toxicomicrobiomics: from scattered reports to systematic studies of drug-microbiome interactions.

Expert opinion on drug metabolism & toxicology, 14(10):1043-1055.

INTRODUCTION: Pharmacomicrobiomics and toxicomicrobiomics study how variations within the human microbiome (the combination of human-associated microbial communities and their genomes) affect drug disposition, action, and toxicity. These emerging fields, interconnecting microbiology, bioinformatics, systems pharmacology, and toxicology, complement pharmacogenomics and toxicogenomics, expanding the scope of precision medicine. Areas covered: This article reviews some of the most recently reported pharmacomicrobiomic and toxicomicrobiomic interactions. Examples include the impact of the human gut microbiota on cardiovascular drugs, natural products, and chemotherapeutic agents, including immune checkpoint inhibitors. Although the gut microbiota has been the most extensively studied, some key drug-microbiome interactions involve vaginal, intratumoral, and environmental bacteria, and are briefly discussed here. Additionally, computational resources, moving the field from cataloging to predicting interactions, are introduced. Expert opinion: The rapid pace of discovery triggered by the Human Microbiome Project is moving pharmacomicrobiomic research from scattered observations to systematic studies focusing on screening microbiome variants against different drug classes. Better representation of all human populations will improve such studies by avoiding sampling bias, and the integration of multiomic studies with designed experiments will allow establishing causation. In the near future, pharmacomicrobiomic testing is expected to be a key step in screening novel drugs and designing precision therapeutics.

@article {pmid30269615,
year = {2018},
author = {Aziz, RK and Hegazy, SM and Yasser, R and Rizkallah, MR and ElRakaiby, MT},
title = {Drug pharmacomicrobiomics and toxicomicrobiomics: from scattered reports to systematic studies of drug-microbiome interactions.},
journal = {Expert opinion on drug metabolism & toxicology},
volume = {14},
number = {10},
pages = {1043-1055},
doi = {10.1080/17425255.2018.1530216},
pmid = {30269615},
issn = {1744-7607},
abstract = {INTRODUCTION: Pharmacomicrobiomics and toxicomicrobiomics study how variations within the human microbiome (the combination of human-associated microbial communities and their genomes) affect drug disposition, action, and toxicity. These emerging fields, interconnecting microbiology, bioinformatics, systems pharmacology, and toxicology, complement pharmacogenomics and toxicogenomics, expanding the scope of precision medicine. Areas covered: This article reviews some of the most recently reported pharmacomicrobiomic and toxicomicrobiomic interactions. Examples include the impact of the human gut microbiota on cardiovascular drugs, natural products, and chemotherapeutic agents, including immune checkpoint inhibitors. Although the gut microbiota has been the most extensively studied, some key drug-microbiome interactions involve vaginal, intratumoral, and environmental bacteria, and are briefly discussed here. Additionally, computational resources, moving the field from cataloging to predicting interactions, are introduced. Expert opinion: The rapid pace of discovery triggered by the Human Microbiome Project is moving pharmacomicrobiomic research from scattered observations to systematic studies focusing on screening microbiome variants against different drug classes. Better representation of all human populations will improve such studies by avoiding sampling bias, and the integration of multiomic studies with designed experiments will allow establishing causation. In the near future, pharmacomicrobiomic testing is expected to be a key step in screening novel drugs and designing precision therapeutics.},
}

RevDate: 2018-09-28

He Y, Wu W, Wu S, et al (2018)

Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis.

Microbiome, 6(1):172 pii:10.1186/s40168-018-0557-6.

BACKGROUND: The metabolic syndrome (MetS) epidemic is associated with economic development, lifestyle transition and dysbiosis of gut microbiota, but these associations are rarely studied at the population scale. Here, we utilised the Guangdong Gut Microbiome Project (GGMP), the largest Eastern population-based gut microbiome dataset covering individuals with different economic statuses, to investigate the relationships between the gut microbiome and host physiology, diet, geography, physical activity and socioeconomic status.

RESULTS: At the population level, 529 OTUs were significantly associated with MetS. OTUs from Proteobacteria and Firmicutes (other than Ruminococcaceae) were mainly positively associated with MetS, whereas those from Bacteroidetes and Ruminococcaceae were negatively associated with MetS. Two hundred fourteen OTUs were significantly associated with host economic status (140 positive and 74 negative associations), and 157 of these OTUs were also MetS associated. A microbial MetS index was formulated to represent the overall gut dysbiosis of MetS. The values of this index were significantly higher in MetS subjects regardless of their economic status or geographical location. The index values did not increase with increasing personal economic status, although the prevalence of MetS was significantly higher in people of higher economic status. With increased economic status, the study population tended to consume more fruits and vegetables and fewer grains, whereas meat consumption was unchanged. Sedentary time was significantly and positively associated with higher economic status. The MetS index showed an additive effect with sedentary lifestyle, as the prevalence of MetS in individuals with high MetS index values and unhealthy lifestyles was significantly higher than that in the rest of the population.

CONCLUSIONS: The gut microbiome is associated with MetS and economic status. A prolonged sedentary lifestyle, rather than Westernised dietary patterns, was the most notable lifestyle change in our Eastern population along with economic development. Moreover, gut dysbiosis and a Western lifestyle had an additive effect on increasing MetS prevalence.

RESULTS: At the population level, 529 OTUs were significantly associated with MetS. OTUs from Proteobacteria and Firmicutes (other than Ruminococcaceae) were mainly positively associated with MetS, whereas those from Bacteroidetes and Ruminococcaceae were negatively associated with MetS. Two hundred fourteen OTUs were significantly associated with host economic status (140 positive and 74 negative associations), and 157 of these OTUs were also MetS associated. A microbial MetS index was formulated to represent the overall gut dysbiosis of MetS. The values of this index were significantly higher in MetS subjects regardless of their economic status or geographical location. The index values did not increase with increasing personal economic status, although the prevalence of MetS was significantly higher in people of higher economic status. With increased economic status, the study population tended to consume more fruits and vegetables and fewer grains, whereas meat consumption was unchanged. Sedentary time was significantly and positively associated with higher economic status. The MetS index showed an additive effect with sedentary lifestyle, as the prevalence of MetS in individuals with high MetS index values and unhealthy lifestyles was significantly higher than that in the rest of the population.

CONCLUSIONS: The gut microbiome is associated with MetS and economic status. A prolonged sedentary lifestyle, rather than Westernised dietary patterns, was the most notable lifestyle change in our Eastern population along with economic development. Moreover, gut dysbiosis and a Western lifestyle had an additive effect on increasing MetS prevalence.},
}

RevDate: 2018-09-19

Little MS, Ervin SM, Walton WG, et al (2018)

Active Site Flexibility Revealed in Crystal Structures of Parabacteroides merdae β-Glucuronidase from the Human Gut Microbiome.

Protein science : a publication of the Protein Society [Epub ahead of print].

β-Glucuronidase (GUS) enzymes in the gastrointestinal tract are involved in maintaining mammalian-microbial symbiosis and can play key roles in drug efficacy and toxicity. Parabacteroides merdae GUS was identified as an abundant mini-Loop 2 (mL2) type GUS enzyme in the Human Microbiome Project gut metagenomic database. Here, we report the crystal structure of P. merdae GUS and highlight the differences between this enzyme and extant structures of gut microbial GUS proteins. We find that P. merdae GUS exhibits a distinct tetrameric quaternary structure and that the mL2 motif traces a unique path within the active site, which also includes two arginines distinctive to this GUS. We observe two states of the P. merdae GUS active site; a loop repositions itself by more than 50 Å to place a functionally-relevant residue into the enzyme's catalytic site. Finally, we find that P. merdae GUS is able to bind to homo- and heteropolymers of the polysaccharide alginic acid. Together, these data broaden our understanding of the structural and functional diversity in the GUS family of enzymes present in the human gut microbiome and point to specialization as an important feature of microbial GUS orthologs. This article is protected by copyright. All rights reserved.

@article {pmid30230652,
year = {2018},
author = {Little, MS and Ervin, SM and Walton, WG and Tripathy, A and Xu, Y and Liu, J and Redinbo, MR},
title = {Active Site Flexibility Revealed in Crystal Structures of Parabacteroides merdae β-Glucuronidase from the Human Gut Microbiome.},
journal = {Protein science : a publication of the Protein Society},
volume = {},
number = {},
pages = {},
doi = {10.1002/pro.3507},
pmid = {30230652},
issn = {1469-896X},
abstract = {β-Glucuronidase (GUS) enzymes in the gastrointestinal tract are involved in maintaining mammalian-microbial symbiosis and can play key roles in drug efficacy and toxicity. Parabacteroides merdae GUS was identified as an abundant mini-Loop 2 (mL2) type GUS enzyme in the Human Microbiome Project gut metagenomic database. Here, we report the crystal structure of P. merdae GUS and highlight the differences between this enzyme and extant structures of gut microbial GUS proteins. We find that P. merdae GUS exhibits a distinct tetrameric quaternary structure and that the mL2 motif traces a unique path within the active site, which also includes two arginines distinctive to this GUS. We observe two states of the P. merdae GUS active site; a loop repositions itself by more than 50 Å to place a functionally-relevant residue into the enzyme's catalytic site. Finally, we find that P. merdae GUS is able to bind to homo- and heteropolymers of the polysaccharide alginic acid. Together, these data broaden our understanding of the structural and functional diversity in the GUS family of enzymes present in the human gut microbiome and point to specialization as an important feature of microbial GUS orthologs. This article is protected by copyright. All rights reserved.},
}

RevDate: 2018-09-13

Xia Y, J Sun (2017)

Hypothesis Testing and Statistical Analysis of Microbiome.

Genes & diseases, 4(3):138-148.

After the initiation of Human Microbiome Project in 2008, various biostatistic and bioinformatic tools for data analysis and computational methods have been developed and applied to microbiome studies. In this review and perspective, we discuss the research and statistical hypotheses in gut microbiome studies, focusing on mechanistic concepts that underlie the complex relationships among host, microbiome, and environment. We review the current available statistic tools and highlight recent progress of newly developed statistical methods and models. Given the current challenges and limitations in biostatistic approaches and tools, we discuss the future direction in developing statistical methods and models for the microbiome studies.

@article {pmid30197908,
year = {2017},
author = {Xia, Y and Sun, J},
title = {Hypothesis Testing and Statistical Analysis of Microbiome.},
journal = {Genes & diseases},
volume = {4},
number = {3},
pages = {138-148},
doi = {10.1016/j.gendis.2017.06.001},
pmid = {30197908},
issn = {2352-4820},
support = {R01 DK105118/DK/NIDDK NIH HHS/United States ; R01 DK114126/DK/NIDDK NIH HHS/United States ; },
abstract = {After the initiation of Human Microbiome Project in 2008, various biostatistic and bioinformatic tools for data analysis and computational methods have been developed and applied to microbiome studies. In this review and perspective, we discuss the research and statistical hypotheses in gut microbiome studies, focusing on mechanistic concepts that underlie the complex relationships among host, microbiome, and environment. We review the current available statistic tools and highlight recent progress of newly developed statistical methods and models. Given the current challenges and limitations in biostatistic approaches and tools, we discuss the future direction in developing statistical methods and models for the microbiome studies.},
}

BACKGROUND: Fetal growth restriction, pre-eclampsia, and pre-term birth are major adverse pregnancy outcomes. These complications are considerable contributors to fetal/maternal morbidity and mortality worldwide. A significant proportion of these cases are thought to be due to dysfunction of the placenta. However, the underlying mechanisms of placental dysfunction are unclear. The aim of the present study was to investigate whether adverse pregnancy outcomes are associated with evidence of placental eukaryotic infection.

RESULTS: We modified the 18S Illumina Amplicon Protocol of the Earth Microbiome Project and made it capable of detecting just a single spiked-in genome copy of Plasmodium falciparum, Saccharomyces cerevisiae, or Toxoplasma gondii among more than 70,000 human cells. Using this method, we were unable to detect eukaryotic pathogens in placental biopsies in instances of adverse pregnancy outcome (n = 199) or in healthy controls (n = 99).

CONCLUSIONS: Eukaryotic infection of the placenta is not an underlying cause of the aforementioned pregnancy complications. Possible clinical applications for this non-targeted, yet extremely sensitive, eukaryotic screening method are manifest.

@article {pmid30172254,
year = {2018},
author = {Lager, S and de Goffau, MC and Sovio, U and Peacock, SJ and Parkhill, J and Charnock-Jones, DS and Smith, GCS},
title = {Detecting eukaryotic microbiota with single-cell sensitivity in human tissue.},
journal = {Microbiome},
volume = {6},
number = {1},
pages = {151},
doi = {10.1186/s40168-018-0529-x},
pmid = {30172254},
issn = {2049-2618},
support = {G1100221//Medical Research Council/United Kingdom ; },
abstract = {BACKGROUND: Fetal growth restriction, pre-eclampsia, and pre-term birth are major adverse pregnancy outcomes. These complications are considerable contributors to fetal/maternal morbidity and mortality worldwide. A significant proportion of these cases are thought to be due to dysfunction of the placenta. However, the underlying mechanisms of placental dysfunction are unclear. The aim of the present study was to investigate whether adverse pregnancy outcomes are associated with evidence of placental eukaryotic infection.

RESULTS: We modified the 18S Illumina Amplicon Protocol of the Earth Microbiome Project and made it capable of detecting just a single spiked-in genome copy of Plasmodium falciparum, Saccharomyces cerevisiae, or Toxoplasma gondii among more than 70,000 human cells. Using this method, we were unable to detect eukaryotic pathogens in placental biopsies in instances of adverse pregnancy outcome (n = 199) or in healthy controls (n = 99).

CONCLUSIONS: Eukaryotic infection of the placenta is not an underlying cause of the aforementioned pregnancy complications. Possible clinical applications for this non-targeted, yet extremely sensitive, eukaryotic screening method are manifest.},
}

SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications-ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.

@article {pmid30155556,
year = {2018},
author = {Ma, ZS},
title = {Sketching the Human Microbiome Biogeography with DAR (Diversity-Area Relationship) Profiles.},
journal = {Microbial ecology},
volume = {},
number = {},
pages = {},
doi = {10.1007/s00248-018-1245-6},
pmid = {30155556},
issn = {1432-184X},
abstract = {SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications-ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography "maps" for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.},
}

RevDate: 2018-08-24

Ma Z, Li L, W Li (2018)

Assessing and Interpreting the Within-Body Biogeography of Human Microbiome Diversity.

Frontiers in microbiology, 9:1619.

A human body hosts a relatively independent microbiome including five major regional biomes (i.e., airway, oral, gut, skin, and urogenital). Each of them may possess different regional characteristics with important implications to our health and diseases (i.e., so-termed microbiome associated diseases). Nevertheless, these regional microbiomes are connected with each other through diffusions and migrations. Here, we investigate the within-body (intra-individual) distribution feature of microbiome diversity via diversity area relationship (DAR) modeling, which, to the best of our knowledge, has not been systematically studied previously. We utilized the Hill numbers for measuring alpha and beta-diversities and built 1,200 within-body DAR models with to date the most comprehensive human microbiome datasets of 18 sites from the human microbiome project (HMP) cohort. We established the intra-DAR profile (z-q pattern: the diversity scaling parameter z of the power law (PL) at diversity order q = 0-3), intra-PDO (pair-wise diversity overlap) profile (g-q), and intra-MAD (maximal accrual diversity) profile (Dmax-q) for the within-body biogeography of the human microbiome. These profiles constitute the "maps" of the within-body biogeography, and offer important insights on the within-body distribution of the human microbiome. Furthermore, we investigated the heterogeneity among individuals in their biogeography parameters and found that there is not an "average Joe" that can represent majority of individuals in a cohort or population. For example, we found that most individuals in the HMP cohort have relatively lower maximal accrual diversity (MAD) or in the "long tail" of the so-termed power law distribution. In the meantime, there are a small number of individuals in the cohort who possess disproportionally higher MAD values. These findings may have important implications for personalized medicine of the human microbiome associated diseases in practice, besides their theoretical significance in microbiome research such as establishing the baseline for the conservation of human microbiome.

@article {pmid30131772,
year = {2018},
author = {Ma, Z and Li, L and Li, W},
title = {Assessing and Interpreting the Within-Body Biogeography of Human Microbiome Diversity.},
journal = {Frontiers in microbiology},
volume = {9},
number = {},
pages = {1619},
doi = {10.3389/fmicb.2018.01619},
pmid = {30131772},
issn = {1664-302X},
abstract = {A human body hosts a relatively independent microbiome including five major regional biomes (i.e., airway, oral, gut, skin, and urogenital). Each of them may possess different regional characteristics with important implications to our health and diseases (i.e., so-termed microbiome associated diseases). Nevertheless, these regional microbiomes are connected with each other through diffusions and migrations. Here, we investigate the within-body (intra-individual) distribution feature of microbiome diversity via diversity area relationship (DAR) modeling, which, to the best of our knowledge, has not been systematically studied previously. We utilized the Hill numbers for measuring alpha and beta-diversities and built 1,200 within-body DAR models with to date the most comprehensive human microbiome datasets of 18 sites from the human microbiome project (HMP) cohort. We established the intra-DAR profile (z-q pattern: the diversity scaling parameter z of the power law (PL) at diversity order q = 0-3), intra-PDO (pair-wise diversity overlap) profile (g-q), and intra-MAD (maximal accrual diversity) profile (Dmax-q) for the within-body biogeography of the human microbiome. These profiles constitute the "maps" of the within-body biogeography, and offer important insights on the within-body distribution of the human microbiome. Furthermore, we investigated the heterogeneity among individuals in their biogeography parameters and found that there is not an "average Joe" that can represent majority of individuals in a cohort or population. For example, we found that most individuals in the HMP cohort have relatively lower maximal accrual diversity (MAD) or in the "long tail" of the so-termed power law distribution. In the meantime, there are a small number of individuals in the cohort who possess disproportionally higher MAD values. These findings may have important implications for personalized medicine of the human microbiome associated diseases in practice, besides their theoretical significance in microbiome research such as establishing the baseline for the conservation of human microbiome.},
}

The human microbiome project via application of metagenomic next-generation sequencing techniques has found surprising large and diverse amounts of microbial sequences across different body sites. There is a wave of investigators studying autoimmune related diseases designing from birth case and control studies to elucidate microbial associations and potential direct triggers. Sequencing analysis, considered big data as it typically includes millions of reads, is challenging but particularly demanding and complex is virome profiling due to its lack of pan-viral genomic signature. Impressively thousands of virus complete genomes have been deposited and these high-quality references are core components of virus profiling pipelines and databases. Still it is commonly known that most viral sequences do not map to known viruses. Moreover human viruses, particularly RNA groups, are notoriously heterogeneous due to high mutation rates. Here, we present the related assembling challenges and a series of bioinformatics steps that were applied in the construction of the complete consensus genome of a novel clinical isolate of Coxsackievirus B1. We further demonstrate our effort in calling mutations between prototype Coxsackievirus B1 sequence from GenBank and serial clinical isolate genome grown in cell culture.

@article {pmid30129002,
year = {2018},
author = {Lin, J and Kimura, BY and Oikarinen, S and Nykter, M},
title = {Bioinformatics Assembling and Assessment of Novel Coxsackievirus B1 Genome.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {1838},
number = {},
pages = {261-272},
doi = {10.1007/978-1-4939-8682-8_18},
pmid = {30129002},
issn = {1940-6029},
abstract = {The human microbiome project via application of metagenomic next-generation sequencing techniques has found surprising large and diverse amounts of microbial sequences across different body sites. There is a wave of investigators studying autoimmune related diseases designing from birth case and control studies to elucidate microbial associations and potential direct triggers. Sequencing analysis, considered big data as it typically includes millions of reads, is challenging but particularly demanding and complex is virome profiling due to its lack of pan-viral genomic signature. Impressively thousands of virus complete genomes have been deposited and these high-quality references are core components of virus profiling pipelines and databases. Still it is commonly known that most viral sequences do not map to known viruses. Moreover human viruses, particularly RNA groups, are notoriously heterogeneous due to high mutation rates. Here, we present the related assembling challenges and a series of bioinformatics steps that were applied in the construction of the complete consensus genome of a novel clinical isolate of Coxsackievirus B1. We further demonstrate our effort in calling mutations between prototype Coxsackievirus B1 sequence from GenBank and serial clinical isolate genome grown in cell culture.},
}

RevDate: 2018-08-12

Kroon SJ, Ravel J, WM Huston (2018)

Cervicovaginal microbiota, women's health, and reproductive outcomes.

Fertility and sterility, 110(3):327-336.

The human microbiome project has shown a remarkable diversity of microbial ecology within the human body. The vaginal microbiota is unique in that in many women it is most often dominated by Lactobacillus species. However, in some women it lacks Lactobacillus spp. and is comprised of a wide array of strict and facultative anaerobes, a state that broadly correlates with increased risk for infection, disease, and poor reproductive and obstetric outcomes. Interestingly, the level of protection against infection can also vary by species and strains of Lactobacillus, and some species although dominant are not always optimal. This factors into the risk of contracting sexually transmitted infections and possibly influences the occurrence of resultant adverse reproductive outcomes such as tubal factor infertility. The composition and function of the vaginal microbiota appear to play an important role in pregnancy and fertility treatment outcomes and future research in this field will shed further translational mechanistic understanding onto the interplay of the vaginal microbiota with women's health and reproduction.

@article {pmid30098679,
year = {2018},
author = {Kroon, SJ and Ravel, J and Huston, WM},
title = {Cervicovaginal microbiota, women's health, and reproductive outcomes.},
journal = {Fertility and sterility},
volume = {110},
number = {3},
pages = {327-336},
doi = {10.1016/j.fertnstert.2018.06.036},
pmid = {30098679},
issn = {1556-5653},
abstract = {The human microbiome project has shown a remarkable diversity of microbial ecology within the human body. The vaginal microbiota is unique in that in many women it is most often dominated by Lactobacillus species. However, in some women it lacks Lactobacillus spp. and is comprised of a wide array of strict and facultative anaerobes, a state that broadly correlates with increased risk for infection, disease, and poor reproductive and obstetric outcomes. Interestingly, the level of protection against infection can also vary by species and strains of Lactobacillus, and some species although dominant are not always optimal. This factors into the risk of contracting sexually transmitted infections and possibly influences the occurrence of resultant adverse reproductive outcomes such as tubal factor infertility. The composition and function of the vaginal microbiota appear to play an important role in pregnancy and fertility treatment outcomes and future research in this field will shed further translational mechanistic understanding onto the interplay of the vaginal microbiota with women's health and reproduction.},
}

RevDate: 2018-10-17

Hanssen EN, Liland KH, Gill P, et al (2018)

Optimizing body fluid recognition from microbial taxonomic profiles.

Forensic science international. Genetics, 37:13-20.

In forensics the DNA-profile is used to identify the person who left a biological trace, but information on body fluid can also be essential in the evidence evaluation process. Microbial composition data could potentially be used for body fluid recognition as an improved alternative to the currently used presumptive tests. We have developed a customized workflow for interpretation of bacterial 16S sequence data based on a model composed of Partial Least Squares (PLS) in combination with Linear Discriminant Analysis (LDA). Large data sets from the Human Microbiome Project (HMP) and the American Gut Project (AGP) were used to test different settings in order to optimize performance. From the initial cross-validation of body fluid recognition within the HMP data, the optimal overall accuracy was close to 98%. Sensitivity values for the fecal and oral samples were ≥0.99, followed by the vaginal samples with 0.98 and the skin and nasal samples with 0.96 and 0.81 respectively. Specificity values were high for all 5 categories, mostly >0.99. This optimal performance was achieved by using the following settings: Taxonomic profiles based on operational taxonomic units (OTUs) with 0.98 identity (OTU98), Aitchisons simplex transform with C = 1 pseudo-count and no regularization (r = 1) in the PLS step. Variable selection did not improve the performance further. To test for robustness across sequencing platforms, we also trained the classifier on HMP data and tested on the AGP data set. In this case, the standard OTU based approach showed moderately decline in accuracy. However, by using taxonomic profiles made by direct assignment of reads to a genus, we were able to nearly maintain the high accuracy levels. The optimal combination of settings was still used, except the taxonomic level being genus instead of OTU98. The performance may be improved even further by using higher resolution taxonomic bins.

@article {pmid30071492,
year = {2018},
author = {Hanssen, EN and Liland, KH and Gill, P and Snipen, L},
title = {Optimizing body fluid recognition from microbial taxonomic profiles.},
journal = {Forensic science international. Genetics},
volume = {37},
number = {},
pages = {13-20},
doi = {10.1016/j.fsigen.2018.07.012},
pmid = {30071492},
issn = {1878-0326},
abstract = {In forensics the DNA-profile is used to identify the person who left a biological trace, but information on body fluid can also be essential in the evidence evaluation process. Microbial composition data could potentially be used for body fluid recognition as an improved alternative to the currently used presumptive tests. We have developed a customized workflow for interpretation of bacterial 16S sequence data based on a model composed of Partial Least Squares (PLS) in combination with Linear Discriminant Analysis (LDA). Large data sets from the Human Microbiome Project (HMP) and the American Gut Project (AGP) were used to test different settings in order to optimize performance. From the initial cross-validation of body fluid recognition within the HMP data, the optimal overall accuracy was close to 98%. Sensitivity values for the fecal and oral samples were ≥0.99, followed by the vaginal samples with 0.98 and the skin and nasal samples with 0.96 and 0.81 respectively. Specificity values were high for all 5 categories, mostly >0.99. This optimal performance was achieved by using the following settings: Taxonomic profiles based on operational taxonomic units (OTUs) with 0.98 identity (OTU98), Aitchisons simplex transform with C = 1 pseudo-count and no regularization (r = 1) in the PLS step. Variable selection did not improve the performance further. To test for robustness across sequencing platforms, we also trained the classifier on HMP data and tested on the AGP data set. In this case, the standard OTU based approach showed moderately decline in accuracy. However, by using taxonomic profiles made by direct assignment of reads to a genus, we were able to nearly maintain the high accuracy levels. The optimal combination of settings was still used, except the taxonomic level being genus instead of OTU98. The performance may be improved even further by using higher resolution taxonomic bins.},
}

RevDate: 2018-09-25

Tripathi A, Marotz C, Gonzalez A, et al (2018)

Are microbiome studies ready for hypothesis-driven research?.

Current opinion in microbiology, 44:61-69.

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.

@article {pmid30059804,
year = {2018},
author = {Tripathi, A and Marotz, C and Gonzalez, A and Vázquez-Baeza, Y and Song, SJ and Bouslimani, A and McDonald, D and Zhu, Q and Sanders, JG and Smarr, L and Dorrestein, PC and Knight, R},
title = {Are microbiome studies ready for hypothesis-driven research?.},
journal = {Current opinion in microbiology},
volume = {44},
number = {},
pages = {61-69},
doi = {10.1016/j.mib.2018.07.002},
pmid = {30059804},
issn = {1879-0364},
support = {R01 HG004872/HG/NHGRI NIH HHS/United States ; R01 HL140976/HL/NHLBI NIH HHS/United States ; R01 MD011389/MD/NIMHD NIH HHS/United States ; },
abstract = {Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.},
}

Atlantic cod (Gadus morhua) provides an interesting species for the study of host-microbe interactions because it lacks the MHC II complex that is involved in the presentation of extracellular pathogens. Nonetheless, little is known about the diversity of its microbiome in natural populations. Here, we use high-throughput sequencing of the 16S rRNA V4 region, amplified with the primer design of the Earth Microbiome Project (EMP), to investigate the microbial composition in gut content and mucosa of 22 adult individuals from two coastal populations in Norway, located 470 km apart. We identify a core microbiome of 23 OTUs (97% sequence similarity) in all individuals that comprises 93% of the total number of reads. The most abundant orders are classified as Vibrionales, Fusobacteriales, Clostridiales, and Bacteroidales. While mucosal samples show significantly lower diversity than gut content samples, no differences in OTU community composition are observed between the two geographically separated populations. All specimens share a limited number of abundant OTUs. Moreover, the most abundant OTU consists of a single oligotype (order Vibrionales, genus Photobacterium) that represents nearly 50% of the reads in both locations. Our results suggest that these microbiomes comprise a limited number of species or that the EMP V4 primers do not yield sufficient resolution to confidently separate these communities. Our study contributes to a growing body of literature that shows limited spatial differentiation of the intestinal microbiomes in marine fish based on 16S rRNA sequencing, highlighting the need for multi-gene approaches to provide more insight into the diversity of these communities.

@article {pmid30057577,
year = {2018},
author = {Riiser, ES and Haverkamp, THA and Borgan, Ø and Jakobsen, KS and Jentoft, S and Star, B},
title = {A Single Vibrionales 16S rRNA Oligotype Dominates the Intestinal Microbiome in Two Geographically Separated Atlantic cod Populations.},
journal = {Frontiers in microbiology},
volume = {9},
number = {},
pages = {1561},
doi = {10.3389/fmicb.2018.01561},
pmid = {30057577},
issn = {1664-302X},
abstract = {Atlantic cod (Gadus morhua) provides an interesting species for the study of host-microbe interactions because it lacks the MHC II complex that is involved in the presentation of extracellular pathogens. Nonetheless, little is known about the diversity of its microbiome in natural populations. Here, we use high-throughput sequencing of the 16S rRNA V4 region, amplified with the primer design of the Earth Microbiome Project (EMP), to investigate the microbial composition in gut content and mucosa of 22 adult individuals from two coastal populations in Norway, located 470 km apart. We identify a core microbiome of 23 OTUs (97% sequence similarity) in all individuals that comprises 93% of the total number of reads. The most abundant orders are classified as Vibrionales, Fusobacteriales, Clostridiales, and Bacteroidales. While mucosal samples show significantly lower diversity than gut content samples, no differences in OTU community composition are observed between the two geographically separated populations. All specimens share a limited number of abundant OTUs. Moreover, the most abundant OTU consists of a single oligotype (order Vibrionales, genus Photobacterium) that represents nearly 50% of the reads in both locations. Our results suggest that these microbiomes comprise a limited number of species or that the EMP V4 primers do not yield sufficient resolution to confidently separate these communities. Our study contributes to a growing body of literature that shows limited spatial differentiation of the intestinal microbiomes in marine fish based on 16S rRNA sequencing, highlighting the need for multi-gene approaches to provide more insight into the diversity of these communities.},
}

RevDate: 2018-08-02

Clayton JB, Al-Ghalith GA, Long HT, et al (2018)

Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate Model.

Scientific reports, 8(1):11159 pii:10.1038/s41598-018-29277-x.

Red-shanked doucs (Pygathrix nemaeus) are endangered, foregut-fermenting colobine primates which are difficult to maintain in captivity. There are critical gaps in our understanding of their natural lifestyle, including dietary habits such as consumption of leaves, unripe fruit, flowers, seeds, and other plant parts. There is also a lack of understanding of enteric adaptations, including their unique microflora. To address these knowledge gaps, we used the douc as a model to study relationships between gastrointestinal microbial community structure and lifestyle. We analyzed published fecal samples as well as detailed dietary history from doucs with four distinct lifestyles (wild, semi-wild, semi-captive, and captive) and determined gastrointestinal bacterial microbiome composition using 16S rRNA sequencing. A clear gradient of microbiome composition was revealed along an axis of natural lifestyle disruption, including significant associations with diet, biodiversity, and microbial function. We also identified potential microbial biomarkers of douc dysbiosis, including Bacteroides and Prevotella, which may be related to health. Our results suggest a gradient-like shift in captivity causes an attendant shift to severe gut dysbiosis, thereby resulting in gastrointestinal issues.

@article {pmid30042392,
year = {2018},
author = {Clayton, JB and Al-Ghalith, GA and Long, HT and Tuan, BV and Cabana, F and Huang, H and Vangay, P and Ward, T and Minh, VV and Tam, NA and Dat, NT and Travis, DA and Murtaugh, MP and Covert, H and Glander, KE and Nadler, T and Toddes, B and Sha, JCM and Singer, R and Knights, D and Johnson, TJ},
title = {Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate Model.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {11159},
doi = {10.1038/s41598-018-29277-x},
pmid = {30042392},
issn = {2045-2322},
support = {T32 DA007097/DA/NIDA NIH HHS/United States ; },
abstract = {Red-shanked doucs (Pygathrix nemaeus) are endangered, foregut-fermenting colobine primates which are difficult to maintain in captivity. There are critical gaps in our understanding of their natural lifestyle, including dietary habits such as consumption of leaves, unripe fruit, flowers, seeds, and other plant parts. There is also a lack of understanding of enteric adaptations, including their unique microflora. To address these knowledge gaps, we used the douc as a model to study relationships between gastrointestinal microbial community structure and lifestyle. We analyzed published fecal samples as well as detailed dietary history from doucs with four distinct lifestyles (wild, semi-wild, semi-captive, and captive) and determined gastrointestinal bacterial microbiome composition using 16S rRNA sequencing. A clear gradient of microbiome composition was revealed along an axis of natural lifestyle disruption, including significant associations with diet, biodiversity, and microbial function. We also identified potential microbial biomarkers of douc dysbiosis, including Bacteroides and Prevotella, which may be related to health. Our results suggest a gradient-like shift in captivity causes an attendant shift to severe gut dysbiosis, thereby resulting in gastrointestinal issues.},
}

Motivation: The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the ﬁrst step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing.

Results: In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efﬁciency. The proposed framework was implemented on Apache Spark, which allows for easy and efﬁcient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can signiﬁcantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437GB) demonstrated the excellent scalability of the proposed method.

Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/˜yijunsun/lab/SLAD.html.

Supplementary information: Supplementary data is available at Bioinformatics online.

@article {pmid30010718,
year = {2018},
author = {Zheng, W and Mao, Q and Genco, RJ and Wactawski-Wende, J and Buck, M and Cai, Y and Sun, Y},
title = {A parallel computational framework for ultra-large-scale sequence clustering analysis.},
journal = {Bioinformatics (Oxford, England)},
volume = {},
number = {},
pages = {},
doi = {10.1093/bioinformatics/bty617},
pmid = {30010718},
issn = {1367-4811},
abstract = {Motivation: The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the ﬁrst step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing.

Results: In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efﬁciency. The proposed framework was implemented on Apache Spark, which allows for easy and efﬁcient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can signiﬁcantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437GB) demonstrated the excellent scalability of the proposed method.

Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/˜yijunsun/lab/SLAD.html.

Supplementary information: Supplementary data is available at Bioinformatics online.},
}

Benefit deriving from the use of light is known since ancient time, but, only in the last decades of twentieth century, we witnessed the rapid expansion of knowledge and techniques. Light-emitted diode (LED)-based devices represent the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The present work reviews the current knowledge about LED-based therapeutic approaches in different skin and hair disorders. LED therapy represents the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The use of LED in the treatment of such conditions has now entered common practice among dermatologists. Additional controlled studies are still needed to corroborate the efficacy of such kind of treatment.

@article {pmid30006754,
year = {2018},
author = {Sorbellini, E and Rucco, M and Rinaldi, F},
title = {Photodynamic and photobiological effects of light-emitting diode (LED) therapy in dermatological disease: an update.},
journal = {Lasers in medical science},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10103-018-2584-8},
pmid = {30006754},
issn = {1435-604X},
abstract = {Benefit deriving from the use of light is known since ancient time, but, only in the last decades of twentieth century, we witnessed the rapid expansion of knowledge and techniques. Light-emitted diode (LED)-based devices represent the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The present work reviews the current knowledge about LED-based therapeutic approaches in different skin and hair disorders. LED therapy represents the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The use of LED in the treatment of such conditions has now entered common practice among dermatologists. Additional controlled studies are still needed to corroborate the efficacy of such kind of treatment.},
}

RevDate: 2018-07-09

Li BL, Cheng L, Zhou XD, et al (2018)

[Research progress on the relationship between oral microbes and digestive system diseases].

The human microbiome project promoted further understanding on human oral microbes. Besides oral diseases such as dental caries, periodontal disease, and oral cancer, oral microbes are closely associated with systematic diseases. They have a close connection with digestive system diseases and even contribute to the origination and progression of colorectal cancer. By reviewing recent studies involving oral microbe-related digestive systemic diseases, we aim to propose the considerable role of oral microbes in relation to digestive systemic diseases and the way of oral microbes to multiple organs of digestive system.

Patents as Early Indicators of Technology and Investment Trends: Analyzing the Microbiome Space as a Case Study.

Frontiers in bioengineering and biotechnology, 6:84.

The human microbiome is the collective of microbes living in symbiosis on and within humans. Modulating its composition and function has become an attractive means for the prevention and treatment of a variety of diseases including cancer. Since the initiation of the human microbiome project in 2007, the number of academic publications and active patent families around the microbiome has grown exponentially. Screening patent databases can be useful for the early detection and the tracking of new technology trends. However, it is not sufficient to assess portfolio sizes because emerging players with small but high-quality patent portfolios will be missed within the noise of large but low-quality portfolio owners. Here we used the consolidated database and software tool PatentSight to benchmark patent portfolios, and to analyze key patent owners and innovators in the microbiome space. Our study shows how in-depth patent analyses combining qualitative and quantitative parameters can identify actionable early indicators of technology and investment trends from large patent datasets.

@article {pmid29974051,
year = {2018},
author = {Fankhauser, M and Moser, C and Nyfeler, T},
title = {Patents as Early Indicators of Technology and Investment Trends: Analyzing the Microbiome Space as a Case Study.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {6},
number = {},
pages = {84},
doi = {10.3389/fbioe.2018.00084},
pmid = {29974051},
issn = {2296-4185},
abstract = {The human microbiome is the collective of microbes living in symbiosis on and within humans. Modulating its composition and function has become an attractive means for the prevention and treatment of a variety of diseases including cancer. Since the initiation of the human microbiome project in 2007, the number of academic publications and active patent families around the microbiome has grown exponentially. Screening patent databases can be useful for the early detection and the tracking of new technology trends. However, it is not sufficient to assess portfolio sizes because emerging players with small but high-quality patent portfolios will be missed within the noise of large but low-quality portfolio owners. Here we used the consolidated database and software tool PatentSight to benchmark patent portfolios, and to analyze key patent owners and innovators in the microbiome space. Our study shows how in-depth patent analyses combining qualitative and quantitative parameters can identify actionable early indicators of technology and investment trends from large patent datasets.},
}

RevDate: 2018-10-10

Coleman M, Elkins C, Gutting B, et al (2018)

Microbiota and Dose Response: Evolving Paradigm of Health Triangle.

Risk analysis : an official publication of the Society for Risk Analysis, 38(10):2013-2028.

SRA Dose-Response and Microbial Risk Analysis Specialty Groups jointly sponsored symposia that addressed the intersections between the "microbiome revolution" and dose response. Invited speakers presented on innovations and advances in gut and nasal microbiota (normal microbial communities) in the first decade after the Human Microbiome Project began. The microbiota and their metabolites are now known to influence health and disease directly and indirectly, through modulation of innate and adaptive immune systems and barrier function. Disruption of healthy microbiota is often associated with changes in abundance and diversity of core microbial species (dysbiosis), caused by stressors including antibiotics, chemotherapy, and disease. Nucleic-acid-based metagenomic methods demonstrated that the dysbiotic host microbiota no longer provide normal colonization resistance to pathogens, a critical component of innate immunity of the superorganism. Diverse pathogens, probiotics, and prebiotics were considered in human and animal models (in vivo and in vitro). Discussion included approaches for design of future microbial dose-response studies to account for the presence of the indigenous microbiota that provide normal colonization resistance, and the absence of the protective microbiota in dysbiosis. As NextGen risk analysis methodology advances with the "microbiome revolution," a proposed new framework, the Health Triangle, may replace the old paradigm based on the Disease Triangle (focused on host, pathogen, and environment) and germophobia. Collaborative experimental designs are needed for testing hypotheses about causality in dose-response relationships for pathogens present in our environments that clearly compete in complex ecosystems with thousands of bacterial species dominating the healthy superorganism.

@article {pmid29900563,
year = {2018},
author = {Coleman, M and Elkins, C and Gutting, B and Mongodin, E and Solano-Aguilar, G and Walls, I},
title = {Microbiota and Dose Response: Evolving Paradigm of Health Triangle.},
journal = {Risk analysis : an official publication of the Society for Risk Analysis},
volume = {38},
number = {10},
pages = {2013-2028},
doi = {10.1111/risa.13121},
pmid = {29900563},
issn = {1539-6924},
abstract = {SRA Dose-Response and Microbial Risk Analysis Specialty Groups jointly sponsored symposia that addressed the intersections between the "microbiome revolution" and dose response. Invited speakers presented on innovations and advances in gut and nasal microbiota (normal microbial communities) in the first decade after the Human Microbiome Project began. The microbiota and their metabolites are now known to influence health and disease directly and indirectly, through modulation of innate and adaptive immune systems and barrier function. Disruption of healthy microbiota is often associated with changes in abundance and diversity of core microbial species (dysbiosis), caused by stressors including antibiotics, chemotherapy, and disease. Nucleic-acid-based metagenomic methods demonstrated that the dysbiotic host microbiota no longer provide normal colonization resistance to pathogens, a critical component of innate immunity of the superorganism. Diverse pathogens, probiotics, and prebiotics were considered in human and animal models (in vivo and in vitro). Discussion included approaches for design of future microbial dose-response studies to account for the presence of the indigenous microbiota that provide normal colonization resistance, and the absence of the protective microbiota in dysbiosis. As NextGen risk analysis methodology advances with the "microbiome revolution," a proposed new framework, the Health Triangle, may replace the old paradigm based on the Disease Triangle (focused on host, pathogen, and environment) and germophobia. Collaborative experimental designs are needed for testing hypotheses about causality in dose-response relationships for pathogens present in our environments that clearly compete in complex ecosystems with thousands of bacterial species dominating the healthy superorganism.},
}

Motivation: Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms' intolerance to each other's presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.

Availability: C++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.asp.

Contact: lealbayr@utmb.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

@article {pmid29878050,
year = {2018},
author = {Albayrak, L and Khanipov, K and Golovko, G and Fofanov, Y},
title = {Detection of Multidimensional Co-Exclusion Patterns in Microbial Communities.},
journal = {Bioinformatics (Oxford, England)},
volume = {},
number = {},
pages = {},
doi = {10.1093/bioinformatics/bty414},
pmid = {29878050},
issn = {1367-4811},
abstract = {Motivation: Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms' intolerance to each other's presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.

Availability: C++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.asp.

Contact: lealbayr@utmb.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.},
}

RevDate: 2018-06-22

Clayton JB, Gomez A, Amato K, et al (2018)

The gut microbiome of nonhuman primates: Lessons in ecology and evolution.

American journal of primatology, 80(6):e22867.

The mammalian gastrointestinal (GI) tract is home to trillions of bacteria that play a substantial role in host metabolism and immunity. While progress has been made in understanding the role that microbial communities play in human health and disease, much less attention has been given to host-associated microbiomes in nonhuman primates (NHPs). Here we review past and current research exploring the gut microbiome of NHPs. First, we summarize methods for characterization of the NHP gut microbiome. Then we discuss variation in gut microbiome composition and function across different NHP taxa. Finally, we highlight how studying the gut microbiome offers new insights into primate nutrition, physiology, and immune system function, as well as enhances our understanding of primate ecology and evolution. Microbiome approaches are useful tools for studying relevant issues in primate ecology. Further study of the gut microbiome of NHPs will offer new insight into primate ecology and evolution as well as human health.

@article {pmid29862519,
year = {2018},
author = {Clayton, JB and Gomez, A and Amato, K and Knights, D and Travis, DA and Blekhman, R and Knight, R and Leigh, S and Stumpf, R and Wolf, T and Glander, KE and Cabana, F and Johnson, TJ},
title = {The gut microbiome of nonhuman primates: Lessons in ecology and evolution.},
journal = {American journal of primatology},
volume = {80},
number = {6},
pages = {e22867},
doi = {10.1002/ajp.22867},
pmid = {29862519},
issn = {1098-2345},
abstract = {The mammalian gastrointestinal (GI) tract is home to trillions of bacteria that play a substantial role in host metabolism and immunity. While progress has been made in understanding the role that microbial communities play in human health and disease, much less attention has been given to host-associated microbiomes in nonhuman primates (NHPs). Here we review past and current research exploring the gut microbiome of NHPs. First, we summarize methods for characterization of the NHP gut microbiome. Then we discuss variation in gut microbiome composition and function across different NHP taxa. Finally, we highlight how studying the gut microbiome offers new insights into primate nutrition, physiology, and immune system function, as well as enhances our understanding of primate ecology and evolution. Microbiome approaches are useful tools for studying relevant issues in primate ecology. Further study of the gut microbiome of NHPs will offer new insight into primate ecology and evolution as well as human health.},
}

Host-associated microbial dynamics are influenced by dietary and immune factors, but how exogenous microbial exposure shapes host-microbe dynamics remains poorly characterized. To investigate this phenomenon, we characterized the skin, rectum, and respiratory tract-associated microbiota in four aquarium-housed dolphins daily over a period of 6 weeks, including administration of a probiotic during weeks 4 to 6. The environmental bacterial sources were also characterized, including the animals' human handlers, the aquarium air and water, and the dolphins' food supply. Continuous microbial exposure occurred between all sites, yet each environment maintained a characteristic microbiota, suggesting that the majority of exposure events do not result in colonization. Small changes in water physicochemistry had a significant but weak correlation with change in dolphin-associated bacterial richness but had no influence on phylogenetic diversity. Food and air microbiota were the richest and had the largest conditional influence on other microbiota in the absence of probiotics, but during probiotic administration, food alone had the largest influence on the stability of the dolphin microbiota. Our results suggest that respiratory tract and gastrointestinal epithelium interactions with air- and food-associated microbes had the biggest influence on host-microbiota dynamics, while other interactions, such as skin transmission, played only a minor role. Finally, direct oral stimulation with a foreign exogenous microbial source can have a profound effect on microbial stability. IMPORTANCE These results provide valuable insights into the ecological influence of exogenous microbial exposure, as well as laying the foundation for improving aquarium management practices. By comparing data for dolphins from aquaria that use natural versus artificial seawater, we demonstrate the potential influence of aquarium water disinfection procedures on dolphin microbial dynamics.

@article {pmid29854953,
year = {2018},
author = {Cardona, C and Lax, S and Larsen, P and Stephens, B and Hampton-Marcell, J and Edwardson, CF and Henry, C and Van Bonn, B and Gilbert, JA},
title = {Environmental Sources of Bacteria Differentially Influence Host-Associated Microbial Dynamics.},
journal = {mSystems},
volume = {3},
number = {3},
pages = {},
doi = {10.1128/mSystems.00052-18},
pmid = {29854953},
issn = {2379-5077},
support = {P30 DK042086/DK/NIDDK NIH HHS/United States ; },
abstract = {Host-associated microbial dynamics are influenced by dietary and immune factors, but how exogenous microbial exposure shapes host-microbe dynamics remains poorly characterized. To investigate this phenomenon, we characterized the skin, rectum, and respiratory tract-associated microbiota in four aquarium-housed dolphins daily over a period of 6 weeks, including administration of a probiotic during weeks 4 to 6. The environmental bacterial sources were also characterized, including the animals' human handlers, the aquarium air and water, and the dolphins' food supply. Continuous microbial exposure occurred between all sites, yet each environment maintained a characteristic microbiota, suggesting that the majority of exposure events do not result in colonization. Small changes in water physicochemistry had a significant but weak correlation with change in dolphin-associated bacterial richness but had no influence on phylogenetic diversity. Food and air microbiota were the richest and had the largest conditional influence on other microbiota in the absence of probiotics, but during probiotic administration, food alone had the largest influence on the stability of the dolphin microbiota. Our results suggest that respiratory tract and gastrointestinal epithelium interactions with air- and food-associated microbes had the biggest influence on host-microbiota dynamics, while other interactions, such as skin transmission, played only a minor role. Finally, direct oral stimulation with a foreign exogenous microbial source can have a profound effect on microbial stability. IMPORTANCE These results provide valuable insights into the ecological influence of exogenous microbial exposure, as well as laying the foundation for improving aquarium management practices. By comparing data for dolphins from aquaria that use natural versus artificial seawater, we demonstrate the potential influence of aquarium water disinfection procedures on dolphin microbial dynamics.},
}

RevDate: 2018-09-25

McDonald D, Hyde E, Debelius JW, et al (2018)

American Gut: an Open Platform for Citizen Science Microbiome Research.

mSystems, 3(3): pii:mSystems00031-18.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

@article {pmid29795809,
year = {2018},
author = {McDonald, D and Hyde, E and Debelius, JW and Morton, JT and Gonzalez, A and Ackermann, G and Aksenov, AA and Behsaz, B and Brennan, C and Chen, Y and DeRight Goldasich, L and Dorrestein, PC and Dunn, RR and Fahimipour, AK and Gaffney, J and Gilbert, JA and Gogul, G and Green, JL and Hugenholtz, P and Humphrey, G and Huttenhower, C and Jackson, MA and Janssen, S and Jeste, DV and Jiang, L and Kelley, ST and Knights, D and Kosciolek, T and Ladau, J and Leach, J and Marotz, C and Meleshko, D and Melnik, AV and Metcalf, JL and Mohimani, H and Montassier, E and Navas-Molina, J and Nguyen, TT and Peddada, S and Pevzner, P and Pollard, KS and Rahnavard, G and Robbins-Pianka, A and Sangwan, N and Shorenstein, J and Smarr, L and Song, SJ and Spector, T and Swafford, AD and Thackray, VG and Thompson, LR and Tripathi, A and Vázquez-Baeza, Y and Vrbanac, A and Wischmeyer, P and Wolfe, E and Zhu, Q and , and Knight, R},
title = {American Gut: an Open Platform for Citizen Science Microbiome Research.},
journal = {mSystems},
volume = {3},
number = {3},
pages = {},
doi = {10.1128/mSystems.00031-18},
pmid = {29795809},
issn = {2379-5077},
support = {//Wellcome Trust/United Kingdom ; P30 DK042086/DK/NIDDK NIH HHS/United States ; P30 DK043351/DK/NIDDK NIH HHS/United States ; },
abstract = {Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.},
}

The human microbiome project (HMP) promoted further understanding of human oral microbes. However, research on the human oral microbiota has not made as much progress as research on the gut microbiota. Currently, the causal relationship between the oral microbiota and oral diseases remains unclear, and little is known about the link between the oral microbiota and human systemic diseases. To further understand the contribution of the oral microbiota in oral diseases and systemic diseases, a Human Oral Microbiome Database (HOMD) was established in the US. The HOMD includes 619 taxa in 13 phyla, and most of the microorganisms are from American populations. Due to individual differences in the microbiome, the HOMD does not reflect the Chinese oral microbial status. Herein, we established a new oral microbiome database-the Oral Microbiome Bank of China (OMBC, http://www.sklod.org/ombc). Currently, the OMBC includes information on 289 bacterial strains and 720 clinical samples from the Chinese population, along with lab and clinical information. The OMBC is the first curated description of a Chinese-associated microbiome; it provides tools for use in investigating the role of the oral microbiome in health and diseases, and will give the community abundant data and strain information for future oral microbial studies.

@article {pmid29760467,
year = {2018},
author = {Xian, P and Xuedong, Z and Xin, X and Yuqing, L and Yan, L and Jiyao, L and Xiaoquan, S and Shi, H and Jian, X and Ga, L},
title = {The Oral Microbiome Bank of China.},
journal = {International journal of oral science},
volume = {10},
number = {2},
pages = {16},
doi = {10.1038/s41368-018-0018-x},
pmid = {29760467},
issn = {2049-3169},
abstract = {The human microbiome project (HMP) promoted further understanding of human oral microbes. However, research on the human oral microbiota has not made as much progress as research on the gut microbiota. Currently, the causal relationship between the oral microbiota and oral diseases remains unclear, and little is known about the link between the oral microbiota and human systemic diseases. To further understand the contribution of the oral microbiota in oral diseases and systemic diseases, a Human Oral Microbiome Database (HOMD) was established in the US. The HOMD includes 619 taxa in 13 phyla, and most of the microorganisms are from American populations. Due to individual differences in the microbiome, the HOMD does not reflect the Chinese oral microbial status. Herein, we established a new oral microbiome database-the Oral Microbiome Bank of China (OMBC, http://www.sklod.org/ombc). Currently, the OMBC includes information on 289 bacterial strains and 720 clinical samples from the Chinese population, along with lab and clinical information. The OMBC is the first curated description of a Chinese-associated microbiome; it provides tools for use in investigating the role of the oral microbiome in health and diseases, and will give the community abundant data and strain information for future oral microbial studies.},
}

The maternal microbiota plays an important role in infant gut colonization. In this work we have investigated which bacterial species are shared across the breast milk, vaginal and stool microbiotas of 109 women shortly before and after giving birth using 16S rRNA gene sequencing and a novel reduced metagenomic sequencing (RMS) approach in a subgroup of 16 women. All the species predicted by the 16S rRNA gene sequencing were also detected by RMS analysis and there was good correspondence between their relative abundances estimated by both approaches. Both approaches also demonstrate a low level of maternal microbiota sharing across the population and RMS analysis identified only two species common to most women and in all sample types (Bifidobacterium longum and Enterococcus faecalis). Breast milk was the only sample type that had significantly higher intra- than inter- individual similarity towards both vaginal and stool samples. We also searched our RMS dataset against an in silico generated reference database derived from bacterial isolates in the Human Microbiome Project. The use of this reference-based search enabled further separation of Bifidobacterium longum into Bifidobacterium longum ssp. longum and Bifidobacterium longum ssp. infantis. We also detected the Lactobacillus rhamnosus GG strain, which was used as a probiotic supplement by some women, demonstrating the potential of RMS approach for deeper taxonomic delineation and estimation.

@article {pmid29724017,
year = {2018},
author = {Avershina, E and Angell, IL and Simpson, M and Storrø, O and Øien, T and Johnsen, R and Rudi, K},
title = {Low Maternal Microbiota Sharing across Gut, Breast Milk and Vagina, as Revealed by 16S rRNA Gene and Reduced Metagenomic Sequencing.},
journal = {Genes},
volume = {9},
number = {5},
pages = {},
doi = {10.3390/genes9050231},
pmid = {29724017},
issn = {2073-4425},
abstract = {The maternal microbiota plays an important role in infant gut colonization. In this work we have investigated which bacterial species are shared across the breast milk, vaginal and stool microbiotas of 109 women shortly before and after giving birth using 16S rRNA gene sequencing and a novel reduced metagenomic sequencing (RMS) approach in a subgroup of 16 women. All the species predicted by the 16S rRNA gene sequencing were also detected by RMS analysis and there was good correspondence between their relative abundances estimated by both approaches. Both approaches also demonstrate a low level of maternal microbiota sharing across the population and RMS analysis identified only two species common to most women and in all sample types (Bifidobacterium longum and Enterococcus faecalis). Breast milk was the only sample type that had significantly higher intra- than inter- individual similarity towards both vaginal and stool samples. We also searched our RMS dataset against an in silico generated reference database derived from bacterial isolates in the Human Microbiome Project. The use of this reference-based search enabled further separation of Bifidobacterium longum into Bifidobacterium longum ssp. longum and Bifidobacterium longum ssp. infantis. We also detected the Lactobacillus rhamnosus GG strain, which was used as a probiotic supplement by some women, demonstrating the potential of RMS approach for deeper taxonomic delineation and estimation.},
}

RevDate: 2018-08-29

Rowan S, A Taylor (2018)

The Role of Microbiota in Retinal Disease.

Advances in experimental medicine and biology, 1074:429-435.

The ten years since the first publications on the human microbiome project have brought enormous attention and insight into the role of the human microbiome in health and disease. Connections between populations of microbiota and ocular disease are now being established, and increased accessibility to microbiome research and insights into other diseases is expected to yield enormous information in the coming years. With the characterization of the ocular microbiome, important insights have already been made regarding corneal and conjunctival tissues. Roles for non-ocular microbiomes in complex retinal diseases are now being evaluated. For example, the gut microbiome has been implicated in the pathogenesis of uveitis. This short review will summarize the few studies linking gut or oral microbiota to diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD). We will also conjecture where the most significant findings still remain to be elucidated. Finally, we will propose the gut-retina axis, related but distinct from the gut-brain axis.

@article {pmid29721973,
year = {2018},
author = {Rowan, S and Taylor, A},
title = {The Role of Microbiota in Retinal Disease.},
journal = {Advances in experimental medicine and biology},
volume = {1074},
number = {},
pages = {429-435},
doi = {10.1007/978-3-319-75402-4_53},
pmid = {29721973},
issn = {0065-2598},
abstract = {The ten years since the first publications on the human microbiome project have brought enormous attention and insight into the role of the human microbiome in health and disease. Connections between populations of microbiota and ocular disease are now being established, and increased accessibility to microbiome research and insights into other diseases is expected to yield enormous information in the coming years. With the characterization of the ocular microbiome, important insights have already been made regarding corneal and conjunctival tissues. Roles for non-ocular microbiomes in complex retinal diseases are now being evaluated. For example, the gut microbiome has been implicated in the pathogenesis of uveitis. This short review will summarize the few studies linking gut or oral microbiota to diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD). We will also conjecture where the most significant findings still remain to be elucidated. Finally, we will propose the gut-retina axis, related but distinct from the gut-brain axis.},
}

RevDate: 2018-05-03

Roche-Lima A, Carrasquillo-Carrión K, Gómez-Moreno R, et al (2018)

The Presence of Genotoxic and/or Pro-inflammatory Bacterial Genes in Gut Metagenomic Databases and Their Possible Link With Inflammatory Bowel Diseases.

Frontiers in genetics, 9:116.

Background: The human gut microbiota is a dynamic community of microorganisms that mediate important biochemical processes. Differences in the gut microbial composition have been associated with inflammatory bowel diseases (IBD) and other intestinal disorders. In this study, we quantified and compared the frequencies of eight genotoxic and/or pro-inflammatory bacterial genes found in metagenomic Whole Genome Sequences (mWGSs) of samples from individuals with IBD vs. a cohort of healthy human subjects. Methods: The eight selected gene sequences were clbN, clbB, cif, cnf-1, usp, tcpC from Escherichia coli, gelE from Enterococcus faecalis and murB from Akkermansia muciniphila. We also included the sequences for the conserved murB genes from E. coli and E. faecalis as markers for the presence of Enterobacteriaceae or Enterococci in the samples. The gene sequences were chosen based on their previously reported ability to disrupt normal cellular processes to either promote inflammation or to cause DNA damage in cultured cells or animal models, which could be linked to a role in IBD. The selected sequences were searched in three different mWGS datasets accessed through the Human Microbiome Project (HMP): a healthy cohort (N = 251), a Crohn's disease cohort (N = 60) and an ulcerative colitis cohort (N = 17). Results: Firstly, the sequences for the murB housekeeping genes from Enterobacteriaceae and Enterococci were more frequently found in the IBD cohorts (32% E. coli in IBD vs. 12% in healthy; 13% E. faecalis in IBD vs. 3% in healthy) than in the healthy cohort, confirming earlier reports of a higher presence of both of these taxa in IBD. For some of the sequences in our study, especially usp and gelE, their frequency was even more sharply increased in the IBD cohorts than in the healthy cohort, suggesting an association with IBD that is not easily explained by the increased presence of E. coli or E. faecalis in those samples. Conclusion: Our results suggest a significant association between the presence of some of these genotoxic or pro-inflammatory gene sequences and IBDs. In addition, these results illustrate the power and limitations of the HMP database in the detection of possible clinical correlations for individual bacterial genes.

@article {pmid29692798,
year = {2018},
author = {Roche-Lima, A and Carrasquillo-Carrión, K and Gómez-Moreno, R and Cruz, JM and Velázquez-Morales, DM and Rogozin, IB and Baerga-Ortiz, A},
title = {The Presence of Genotoxic and/or Pro-inflammatory Bacterial Genes in Gut Metagenomic Databases and Their Possible Link With Inflammatory Bowel Diseases.},
journal = {Frontiers in genetics},
volume = {9},
number = {},
pages = {116},
doi = {10.3389/fgene.2018.00116},
pmid = {29692798},
issn = {1664-8021},
support = {U54 MD007600/MD/NIMHD NIH HHS/United States ; R25 GM061838/GM/NIGMS NIH HHS/United States ; G12 RR003051/RR/NCRR NIH HHS/United States ; G12 MD007600/MD/NIMHD NIH HHS/United States ; R21 CA198963/CA/NCI NIH HHS/United States ; },
abstract = {Background: The human gut microbiota is a dynamic community of microorganisms that mediate important biochemical processes. Differences in the gut microbial composition have been associated with inflammatory bowel diseases (IBD) and other intestinal disorders. In this study, we quantified and compared the frequencies of eight genotoxic and/or pro-inflammatory bacterial genes found in metagenomic Whole Genome Sequences (mWGSs) of samples from individuals with IBD vs. a cohort of healthy human subjects. Methods: The eight selected gene sequences were clbN, clbB, cif, cnf-1, usp, tcpC from Escherichia coli, gelE from Enterococcus faecalis and murB from Akkermansia muciniphila. We also included the sequences for the conserved murB genes from E. coli and E. faecalis as markers for the presence of Enterobacteriaceae or Enterococci in the samples. The gene sequences were chosen based on their previously reported ability to disrupt normal cellular processes to either promote inflammation or to cause DNA damage in cultured cells or animal models, which could be linked to a role in IBD. The selected sequences were searched in three different mWGS datasets accessed through the Human Microbiome Project (HMP): a healthy cohort (N = 251), a Crohn's disease cohort (N = 60) and an ulcerative colitis cohort (N = 17). Results: Firstly, the sequences for the murB housekeeping genes from Enterobacteriaceae and Enterococci were more frequently found in the IBD cohorts (32% E. coli in IBD vs. 12% in healthy; 13% E. faecalis in IBD vs. 3% in healthy) than in the healthy cohort, confirming earlier reports of a higher presence of both of these taxa in IBD. For some of the sequences in our study, especially usp and gelE, their frequency was even more sharply increased in the IBD cohorts than in the healthy cohort, suggesting an association with IBD that is not easily explained by the increased presence of E. coli or E. faecalis in those samples. Conclusion: Our results suggest a significant association between the presence of some of these genotoxic or pro-inflammatory gene sequences and IBDs. In addition, these results illustrate the power and limitations of the HMP database in the detection of possible clinical correlations for individual bacterial genes.},
}

Motivation: Comparisons of microbiome communities across populations are often based on pairwise distance measures (beta-diversity). Standard analyses (principal coordinate plots, permutation tests, kernel methods) require access to primary data if another investigator wants to add or compare independent data. We propose using standard reference measurements to simplify microbiome beta-diversity analyses, to make them more transparent, and to facilitate independent validation and comparisons across studies.

Results: Using stool and nasal reference sets from the Human Microbiome Project (HMP), we computed mean distances (actually Bray-Curtis or Pearson correlation dissimilarities) to each reference set for each new sample. Thus, each new sample has two mean distances that can be plotted and analyzed with classical statistical methods. To test the approach, we studied independent (not reference) HMP subjects. Simple Hotelling tests demonstrated statistically significant differences in mean distances to reference sets between all pairs of body sites (stool, skin, nasal, saliva and vagina) at the phylum, class, order, family and genus levels. Using the distance to a single reference set was usually sufficient, but using both reference sets always worked well. The use of reference sets simplifies standard analyses of beta-diversity and facilitates the independent validation and combining of such data because others can compute distances to the same reference sets. Moreover, standard statistical methods for survival analysis, logistic regression and other procedures can be applied to vectors of mean distances to reference sets, thereby greatly expanding the potential uses of beta-diversity information. More work is needed to identify the best reference sets for particular applications.

https://github.com/NCI-biostats/microbiome-fixed-reference.

Supplementary information: Supplementary data are available at Bioinformatics online.

Results: Using stool and nasal reference sets from the Human Microbiome Project (HMP), we computed mean distances (actually Bray-Curtis or Pearson correlation dissimilarities) to each reference set for each new sample. Thus, each new sample has two mean distances that can be plotted and analyzed with classical statistical methods. To test the approach, we studied independent (not reference) HMP subjects. Simple Hotelling tests demonstrated statistically significant differences in mean distances to reference sets between all pairs of body sites (stool, skin, nasal, saliva and vagina) at the phylum, class, order, family and genus levels. Using the distance to a single reference set was usually sufficient, but using both reference sets always worked well. The use of reference sets simplifies standard analyses of beta-diversity and facilitates the independent validation and combining of such data because others can compute distances to the same reference sets. Moreover, standard statistical methods for survival analysis, logistic regression and other procedures can be applied to vectors of mean distances to reference sets, thereby greatly expanding the potential uses of beta-diversity information. More work is needed to identify the best reference sets for particular applications.

https://github.com/NCI-biostats/microbiome-fixed-reference.

Supplementary information: Supplementary data are available at Bioinformatics online.},
}

We introduce GATTACA, a framework for fast unsupervised binning of metagenomic contigs. Similar to recent approaches, GATTACA clusters contigs based on their coverage profiles across a large cohort of metagenomic samples; however, unlike previous methods that rely on read mapping, GATTACA quickly estimates these profiles from kmer counts stored in a compact index. This approach can result in over an order of magnitude speedup, while matching the accuracy of earlier methods on synthetic and real data benchmarks. It also provides a way to index metagenomic samples (e.g., from public repositories such as the Human Microbiome Project) offline once and reuse them across experiments; furthermore, the small size of the sample indices allows them to be easily transferred and stored. Leveraging the MinHash technique, GATTACA also provides an efficient way to identify publicly available metagenomic data that can be incorporated into the set of reference metagenomes to further improve binning accuracy. Thus, enabling easy indexing and reuse of publicly available metagenomic data sets, GATTACA makes accurate metagenomic analyses accessible to a much wider range of researchers.

@article {pmid29658784,
year = {2018},
author = {Popic, V and Kuleshov, V and Snyder, M and Batzoglou, S},
title = {Fast Metagenomic Binning via Hashing and Bayesian Clustering.},
journal = {Journal of computational biology : a journal of computational molecular cell biology},
volume = {25},
number = {7},
pages = {677-688},
doi = {10.1089/cmb.2017.0250},
pmid = {29658784},
issn = {1557-8666},
abstract = {We introduce GATTACA, a framework for fast unsupervised binning of metagenomic contigs. Similar to recent approaches, GATTACA clusters contigs based on their coverage profiles across a large cohort of metagenomic samples; however, unlike previous methods that rely on read mapping, GATTACA quickly estimates these profiles from kmer counts stored in a compact index. This approach can result in over an order of magnitude speedup, while matching the accuracy of earlier methods on synthetic and real data benchmarks. It also provides a way to index metagenomic samples (e.g., from public repositories such as the Human Microbiome Project) offline once and reuse them across experiments; furthermore, the small size of the sample indices allows them to be easily transferred and stored. Leveraging the MinHash technique, GATTACA also provides an efficient way to identify publicly available metagenomic data that can be incorporated into the set of reference metagenomes to further improve binning accuracy. Thus, enabling easy indexing and reuse of publicly available metagenomic data sets, GATTACA makes accurate metagenomic analyses accessible to a much wider range of researchers.},
}

Temporal variation in microbiome measurements can reduce statistical power in research studies. Quantification of this variation is essential for designing studies of chronic disease. We analyzed 16S ribosomal RNA profiles in paired biological specimens separated by 6 months from 3 studies conducted during 1985-2013 (a National Cancer Institute colorectal cancer study, a Costa Rica study, and the Human Microbiome Project). We evaluated temporal stability by calculating intraclass correlation coefficients (ICCs). Sample sizes needed in order to detect microbiome differences between equal numbers of cases and controls for a nested case-control design were calculated on the basis of estimated ICCs. Across body sites, 12 phylum-level ICCs were greater than 0.5. Similarly, 11 alpha-diversity ICCs were greater than 0.5. Fecal beta-diversity estimates had ICCs over 0.5. For a single collection with most microbiome metrics, detecting an odds ratio of 2.0 would require 300-500 cases when matching 1 case to 1 control at P = 0.05. Use of 2 or 3 sequential specimens reduces the number of required subjects by 40%-50% for low-ICC metrics. Relative abundances of major phyla and alpha-diversity metrics have low temporal stability. Thus, detecting associations of moderate effect size with these metrics will require large sample sizes. Because beta diversity for feces is reasonably stable over time, smaller sample sizes can detect associations with community composition. Sequential prediagnostic specimens from thousands of prospectively ascertained cases are required to detect modest disease associations with particular microbiome metrics.

@article {pmid29608646,
year = {2018},
author = {Sinha, R and Goedert, JJ and Vogtmann, E and Hua, X and Porras, C and Hayes, R and Safaeian, M and Yu, G and Sampson, J and Ahn, J and Shi, J},
title = {Quantification of Human Microbiome Stability Over 6 Months: Implications for Epidemiologic Studies.},
journal = {American journal of epidemiology},
volume = {187},
number = {6},
pages = {1282-1290},
doi = {10.1093/aje/kwy064},
pmid = {29608646},
issn = {1476-6256},
support = {R03 CA159414/CA/NCI NIH HHS/United States ; },
abstract = {Temporal variation in microbiome measurements can reduce statistical power in research studies. Quantification of this variation is essential for designing studies of chronic disease. We analyzed 16S ribosomal RNA profiles in paired biological specimens separated by 6 months from 3 studies conducted during 1985-2013 (a National Cancer Institute colorectal cancer study, a Costa Rica study, and the Human Microbiome Project). We evaluated temporal stability by calculating intraclass correlation coefficients (ICCs). Sample sizes needed in order to detect microbiome differences between equal numbers of cases and controls for a nested case-control design were calculated on the basis of estimated ICCs. Across body sites, 12 phylum-level ICCs were greater than 0.5. Similarly, 11 alpha-diversity ICCs were greater than 0.5. Fecal beta-diversity estimates had ICCs over 0.5. For a single collection with most microbiome metrics, detecting an odds ratio of 2.0 would require 300-500 cases when matching 1 case to 1 control at P = 0.05. Use of 2 or 3 sequential specimens reduces the number of required subjects by 40%-50% for low-ICC metrics. Relative abundances of major phyla and alpha-diversity metrics have low temporal stability. Thus, detecting associations of moderate effect size with these metrics will require large sample sizes. Because beta diversity for feces is reasonably stable over time, smaller sample sizes can detect associations with community composition. Sequential prediagnostic specimens from thousands of prospectively ascertained cases are required to detect modest disease associations with particular microbiome metrics.},
}

RevDate: 2018-03-25

Flores Saiffe Farías A, Mendizabal AP, JA Morales (2018)

An Ontology Systems Approach on Human Brain Expression and Metaproteomics.

Frontiers in microbiology, 9:406.

Research in the last decade has shown growing evidence of the gut microbiota influence on brain physiology. While many mechanisms of this influence have been proposed in animal models, most studies in humans are the result of a pathology-dysbiosis association and very few have related the presence of certain taxa with brain substructures or molecular pathways. In this paper, we associated the functional ontologies in the differential expression of brain substructures from the Allen Brain Atlas database, with those of the metaproteome from the Human Microbiome Project. Our results showed several coherent clustered ontologies where many taxa could influence brain expression and physiology. A detailed analysis of psychobiotics showed specific slim ontologies functionally associated with substructures in the basal ganglia and cerebellar cortex. Some of the most relevant slim ontology groups are related to Ion transport, Membrane potential, Synapse, DNA and RNA metabolism, and Antigen processing, while the most relevant neuropathology found was Parkinson disease. In some of these cases, new hypothetical gut microbiota-brain interaction pathways are proposed.

@article {pmid29568289,
year = {2018},
author = {Flores Saiffe Farías, A and Mendizabal, AP and Morales, JA},
title = {An Ontology Systems Approach on Human Brain Expression and Metaproteomics.},
journal = {Frontiers in microbiology},
volume = {9},
number = {},
pages = {406},
doi = {10.3389/fmicb.2018.00406},
pmid = {29568289},
issn = {1664-302X},
abstract = {Research in the last decade has shown growing evidence of the gut microbiota influence on brain physiology. While many mechanisms of this influence have been proposed in animal models, most studies in humans are the result of a pathology-dysbiosis association and very few have related the presence of certain taxa with brain substructures or molecular pathways. In this paper, we associated the functional ontologies in the differential expression of brain substructures from the Allen Brain Atlas database, with those of the metaproteome from the Human Microbiome Project. Our results showed several coherent clustered ontologies where many taxa could influence brain expression and physiology. A detailed analysis of psychobiotics showed specific slim ontologies functionally associated with substructures in the basal ganglia and cerebellar cortex. Some of the most relevant slim ontology groups are related to Ion transport, Membrane potential, Synapse, DNA and RNA metabolism, and Antigen processing, while the most relevant neuropathology found was Parkinson disease. In some of these cases, new hypothetical gut microbiota-brain interaction pathways are proposed.},
}

RevDate: 2018-09-28

Lang JM, Coil DA, Neches RY, et al (2017)

A microbial survey of the International Space Station (ISS).

PeerJ, 5:e4029 pii:4029.

Background: Modern advances in sequencing technology have enabled the census of microbial members of many natural ecosystems. Recently, attention is increasingly being paid to the microbial residents of human-made, built ecosystems, both private (homes) and public (subways, office buildings, and hospitals). Here, we report results of the characterization of the microbial ecology of a singular built environment, the International Space Station (ISS). This ISS sampling involved the collection and microbial analysis (via 16S rDNA PCR) of 15 surfaces sampled by swabs onboard the ISS. This sampling was a component of Project MERCCURI (Microbial Ecology Research Combining Citizen and University Researchers on ISS). Learning more about the microbial inhabitants of the "buildings" in which we travel through space will take on increasing importance, as plans for human exploration continue, with the possibility of colonization of other planets and moons.

Results: Sterile swabs were used to sample 15 surfaces onboard the ISS. The sites sampled were designed to be analogous to samples collected for (1) the Wildlife of Our Homes project and (2) a study of cell phones and shoes that were concurrently being collected for another component of Project MERCCURI. Sequencing of the 16S rDNA genes amplified from DNA extracted from each swab was used to produce a census of the microbes present on each surface sampled. We compared the microbes found on the ISS swabs to those from both homes on Earth and data from the Human Microbiome Project.

Conclusions: While significantly different from homes on Earth and the Human Microbiome Project samples analyzed here, the microbial community composition on the ISS was more similar to home surfaces than to the human microbiome samples. The ISS surfaces are species-rich with 1,036-4,294 operational taxonomic units (OTUs per sample). There was no discernible biogeography of microbes on the 15 ISS surfaces, although this may be a reflection of the small sample size we were able to obtain.

@article {pmid29492330,
year = {2017},
author = {Lang, JM and Coil, DA and Neches, RY and Brown, WE and Cavalier, D and Severance, M and Hampton-Marcell, JT and Gilbert, JA and Eisen, JA},
title = {A microbial survey of the International Space Station (ISS).},
journal = {PeerJ},
volume = {5},
number = {},
pages = {e4029},
doi = {10.7717/peerj.4029},
pmid = {29492330},
issn = {2167-8359},
support = {P30 DK042086/DK/NIDDK NIH HHS/United States ; },
abstract = {Background: Modern advances in sequencing technology have enabled the census of microbial members of many natural ecosystems. Recently, attention is increasingly being paid to the microbial residents of human-made, built ecosystems, both private (homes) and public (subways, office buildings, and hospitals). Here, we report results of the characterization of the microbial ecology of a singular built environment, the International Space Station (ISS). This ISS sampling involved the collection and microbial analysis (via 16S rDNA PCR) of 15 surfaces sampled by swabs onboard the ISS. This sampling was a component of Project MERCCURI (Microbial Ecology Research Combining Citizen and University Researchers on ISS). Learning more about the microbial inhabitants of the "buildings" in which we travel through space will take on increasing importance, as plans for human exploration continue, with the possibility of colonization of other planets and moons.

Results: Sterile swabs were used to sample 15 surfaces onboard the ISS. The sites sampled were designed to be analogous to samples collected for (1) the Wildlife of Our Homes project and (2) a study of cell phones and shoes that were concurrently being collected for another component of Project MERCCURI. Sequencing of the 16S rDNA genes amplified from DNA extracted from each swab was used to produce a census of the microbes present on each surface sampled. We compared the microbes found on the ISS swabs to those from both homes on Earth and data from the Human Microbiome Project.

Conclusions: While significantly different from homes on Earth and the Human Microbiome Project samples analyzed here, the microbial community composition on the ISS was more similar to home surfaces than to the human microbiome samples. The ISS surfaces are species-rich with 1,036-4,294 operational taxonomic units (OTUs per sample). There was no discernible biogeography of microbes on the 15 ISS surfaces, although this may be a reflection of the small sample size we were able to obtain.},
}

RevDate: 2018-03-01

Wu WK, Chen CC, Panyod S, et al (2018)

Optimization of fecal sample processing for microbiome study - The journey from bathroom to bench.

Although great interest has been displayed by researchers in the contribution of gut microbiota to human health, there is still no standard protocol with consensus to guarantee the sample quality of metagenomic analysis. Here we reviewed existing methodology studies and present suggestions for optimizing research pipeline from fecal sample collection to DNA extraction. First, we discuss strategies of clinical metadata collection as common confounders for microbiome research. Second, we propose general principles for freshly collected fecal sample and its storage and share a DIY stool collection kit protocol based on the manual procedure of Human Microbiome Project (HMP). Third, we provide a useful information of collection kit with DNA stabilization buffers and compare their pros and cons for multi-omic study. Fourth, we offer technical strategies as well as information of novel tools for sample aliquoting before long-term storage. Fifth, we discuss the substantial impact of different DNA extraction protocols on technical variations of metagenomic analysis. And lastly, we point out the limitation of current methods and the unmet needs for better quality control of metagenomic analysis. We hope the information provided here will help investigators in this exciting field to advance their studies while avoiding experimental artifacts.

@article {pmid29490879,
year = {2018},
author = {Wu, WK and Chen, CC and Panyod, S and Chen, RA and Wu, MS and Sheen, LY and Chang, SC},
title = {Optimization of fecal sample processing for microbiome study - The journey from bathroom to bench.},
journal = {Journal of the Formosan Medical Association = Taiwan yi zhi},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jfma.2018.02.005},
pmid = {29490879},
issn = {0929-6646},
abstract = {Although great interest has been displayed by researchers in the contribution of gut microbiota to human health, there is still no standard protocol with consensus to guarantee the sample quality of metagenomic analysis. Here we reviewed existing methodology studies and present suggestions for optimizing research pipeline from fecal sample collection to DNA extraction. First, we discuss strategies of clinical metadata collection as common confounders for microbiome research. Second, we propose general principles for freshly collected fecal sample and its storage and share a DIY stool collection kit protocol based on the manual procedure of Human Microbiome Project (HMP). Third, we provide a useful information of collection kit with DNA stabilization buffers and compare their pros and cons for multi-omic study. Fourth, we offer technical strategies as well as information of novel tools for sample aliquoting before long-term storage. Fifth, we discuss the substantial impact of different DNA extraction protocols on technical variations of metagenomic analysis. And lastly, we point out the limitation of current methods and the unmet needs for better quality control of metagenomic analysis. We hope the information provided here will help investigators in this exciting field to advance their studies while avoiding experimental artifacts.},
}

RevDate: 2018-09-18

Kolde R, Franzosa EA, Rahnavard G, et al (2018)

Host genetic variation and its microbiome interactions within the Human Microbiome Project.

Genome medicine, 10(1):6 pii:10.1186/s13073-018-0515-8.

BACKGROUND: Despite the increasing recognition that microbial communities within the human body are linked to health, we have an incomplete understanding of the environmental and molecular interactions that shape the composition of these communities. Although host genetic factors play a role in these interactions, these factors have remained relatively unexplored given the requirement for large population-based cohorts in which both genotyping and microbiome characterization have been performed.

METHODS: We performed whole-genome sequencing of 298 donors from the Human Microbiome Project (HMP) healthy cohort study to accompany existing deep characterization of their microbiomes at various body sites. This analysis yielded an average sequencing depth of 32x, with which we identified 27 million (M) single nucleotide variants and 2.3 M insertions-deletions.

RESULTS: Taxonomic composition and functional potential of the microbiome covaried significantly with genetic principal components in the gastrointestinal tract and oral communities, but not in the nares or vaginal microbiota. Example associations included validation of known associations between FUT2 secretor status, as well as a variant conferring hypolactasia near the LCT gene, with Bifidobacterium longum abundance in stool. The associations of microbial features with both high-level genetic attributes and single variants were specific to particular body sites, highlighting the opportunity to find unique genetic mechanisms controlling microbiome properties in the microbial communities from multiple body sites.

CONCLUSIONS: This study adds deep sequencing of host genomes to the body-wide microbiome sequences already extant from the HMP healthy cohort, creating a unique, versatile, and well-controlled reference for future studies seeking to identify host genetic modulators of the microbiome.

@article {pmid29378630,
year = {2018},
author = {Kolde, R and Franzosa, EA and Rahnavard, G and Hall, AB and Vlamakis, H and Stevens, C and Daly, MJ and Xavier, RJ and Huttenhower, C},
title = {Host genetic variation and its microbiome interactions within the Human Microbiome Project.},
journal = {Genome medicine},
volume = {10},
number = {1},
pages = {6},
doi = {10.1186/s13073-018-0515-8},
pmid = {29378630},
issn = {1756-994X},
support = {U54 DK102557/DK/NIDDK NIH HHS/United States ; U54 HG003067/HG/NHGRI NIH HHS/United States ; P30 DK043351/DK/NIDDK NIH HHS/United States ; 5U54HG003067-13/HG/NHGRI NIH HHS/United States ; U54 DE023798/DE/NIDCR NIH HHS/United States ; },
abstract = {BACKGROUND: Despite the increasing recognition that microbial communities within the human body are linked to health, we have an incomplete understanding of the environmental and molecular interactions that shape the composition of these communities. Although host genetic factors play a role in these interactions, these factors have remained relatively unexplored given the requirement for large population-based cohorts in which both genotyping and microbiome characterization have been performed.

METHODS: We performed whole-genome sequencing of 298 donors from the Human Microbiome Project (HMP) healthy cohort study to accompany existing deep characterization of their microbiomes at various body sites. This analysis yielded an average sequencing depth of 32x, with which we identified 27 million (M) single nucleotide variants and 2.3 M insertions-deletions.

RESULTS: Taxonomic composition and functional potential of the microbiome covaried significantly with genetic principal components in the gastrointestinal tract and oral communities, but not in the nares or vaginal microbiota. Example associations included validation of known associations between FUT2 secretor status, as well as a variant conferring hypolactasia near the LCT gene, with Bifidobacterium longum abundance in stool. The associations of microbial features with both high-level genetic attributes and single variants were specific to particular body sites, highlighting the opportunity to find unique genetic mechanisms controlling microbiome properties in the microbial communities from multiple body sites.

BACKGROUND: Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.

RESULTS: This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis.

CONCLUSIONS: SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).

@article {pmid29373999,
year = {2018},
author = {Fuks, G and Elgart, M and Amir, A and Zeisel, A and Turnbaugh, PJ and Soen, Y and Shental, N},
title = {Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling.},
journal = {Microbiome},
volume = {6},
number = {1},
pages = {17},
doi = {10.1186/s40168-017-0396-x},
pmid = {29373999},
issn = {2049-2618},
support = {R01 HL122593/HL/NHLBI NIH HHS/United States ; 3-11174//Ministry of Science and Technology, Israel/International ; },
abstract = {BACKGROUND: Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.

RESULTS: This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis.

CONCLUSIONS: SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).},
}

Microbiome studies show altered microbiota in head and neck squamous cell carcinoma (HNSCC), both in terms of taxonomic composition and metabolic capacity. These studies utilized a traditional bioinformatics methodology, which allows for accurate taxonomic assignment down to the genus level, but cannot accurately resolve species level membership. We applied Resphera Insight, a high-resolution methodology for 16S rRNA taxonomic assignment that is able to provide species-level context in its assignments of 16S rRNA next generation sequencing (NGS) data. Resphera Insight applied to saliva samples from HNSCC patients and healthy controls led to the discovery that a subset of HNSCC saliva samples is significantly enriched with commensal species from the vaginal flora, including Lactobacillus gasseri/johnsonii (710x higher in saliva) and Lactobacillus vaginalis (52x higher in saliva). These species were not observed in normal saliva from Johns Hopkins patients, nor in 16S rRNA NGS saliva samples from the Human Microbiome Project (HMP). Interestingly, both species were only observed in saliva from Human Papilloma Virus (HPV) positive and HPV negative oropharyngeal cancer patients. We confirmed the representation of both species in HMP data obtained from mid-vagina (n=128) and vaginal introitus (n=121) samples. Resphera Insight also led to the discovery that Fusobacterium nucleatum, an oral cavity flora commensal bacterium linked to colon cancer, is enriched (600x higher) in saliva from a subset of HNSCC patients with advanced tumors stages. Together, these high-resolution analyses on 583 samples suggest a possible role for bacterial species in the therapeutic outcome of HPV positive and HPV negative HNSCC patients.

@article {pmid29340028,
year = {2017},
author = {Guerrero-Preston, R and White, JR and Godoy-Vitorino, F and Rodríguez-Hilario, A and Navarro, K and González, H and Michailidi, C and Jedlicka, A and Canapp, S and Bondy, J and Dziedzic, A and Mora-Lagos, B and Rivera-Alvarez, G and Ili-Gangas, C and Brebi-Mieville, P and Westra, W and Koch, W and Kang, H and Marchionni, L and Kim, Y and Sidransky, D},
title = {High-resolution microbiome profiling uncovers Fusobacterium nucleatum, Lactobacillus gasseri/johnsonii, and Lactobacillus vaginalis associated to oral and oropharyngeal cancer in saliva from HPV positive and HPV negative patients treated with surgery and chemo-radiation.},
journal = {Oncotarget},
volume = {8},
number = {67},
pages = {110931-110948},
doi = {10.18632/oncotarget.20677},
pmid = {29340028},
issn = {1949-2553},
support = {P20 GM103475/GM/NIGMS NIH HHS/United States ; R01 CA121113/CA/NCI NIH HHS/United States ; K01 CA164092/CA/NCI NIH HHS/United States ; P50 DE019032/DE/NIDCR NIH HHS/United States ; RC2 DE020957/DE/NIDCR NIH HHS/United States ; U01 CA084986/CA/NCI NIH HHS/United States ; },
abstract = {Microbiome studies show altered microbiota in head and neck squamous cell carcinoma (HNSCC), both in terms of taxonomic composition and metabolic capacity. These studies utilized a traditional bioinformatics methodology, which allows for accurate taxonomic assignment down to the genus level, but cannot accurately resolve species level membership. We applied Resphera Insight, a high-resolution methodology for 16S rRNA taxonomic assignment that is able to provide species-level context in its assignments of 16S rRNA next generation sequencing (NGS) data. Resphera Insight applied to saliva samples from HNSCC patients and healthy controls led to the discovery that a subset of HNSCC saliva samples is significantly enriched with commensal species from the vaginal flora, including Lactobacillus gasseri/johnsonii (710x higher in saliva) and Lactobacillus vaginalis (52x higher in saliva). These species were not observed in normal saliva from Johns Hopkins patients, nor in 16S rRNA NGS saliva samples from the Human Microbiome Project (HMP). Interestingly, both species were only observed in saliva from Human Papilloma Virus (HPV) positive and HPV negative oropharyngeal cancer patients. We confirmed the representation of both species in HMP data obtained from mid-vagina (n=128) and vaginal introitus (n=121) samples. Resphera Insight also led to the discovery that Fusobacterium nucleatum, an oral cavity flora commensal bacterium linked to colon cancer, is enriched (600x higher) in saliva from a subset of HNSCC patients with advanced tumors stages. Together, these high-resolution analyses on 583 samples suggest a possible role for bacterial species in the therapeutic outcome of HPV positive and HPV negative HNSCC patients.},
}

RevDate: 2018-09-13

Schirmer M, Franzosa EA, Lloyd-Price J, et al (2018)

Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.

Nature microbiology, 3(3):337-346.

Inflammatory bowel disease (IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. Genetic, environmental and microbial factors have been implicated in the onset and exacerbation of IBD. However, the mechanisms associating gut microbial dysbioses and aberrant immune responses remain largely unknown. The integrative Human Microbiome Project seeks to close these gaps by examining the dynamics of microbiome functionality in disease by profiling the gut microbiomes of >100 individuals sampled over a 1-year period. Here, we present the first results based on 78 paired faecal metagenomes and metatranscriptomes, and 222 additional metagenomes from 59 patients with Crohn's disease, 34 with ulcerative colitis and 24 non-IBD control patients. We demonstrate several cases in which measures of microbial gene expression in the inflamed gut can be informative relative to metagenomic profiles of functional potential. First, although many microbial organisms exhibited concordant DNA and RNA abundances, we also detected species-specific biases in transcriptional activity, revealing predominant transcription of pathways by individual microorganisms per host (for example, by Faecalibacterium prausnitzii). Thus, a loss of these organisms in disease may have more far-reaching consequences than suggested by their genomic abundances. Furthermore, we identified organisms that were metagenomically abundant but inactive or dormant in the gut with little or no expression (for example, Dialister invisus). Last, certain disease-specific microbial characteristics were more pronounced or only detectable at the transcript level, such as pathways that were predominantly expressed by different organisms in patients with IBD (for example, Bacteroides vulgatus and Alistipes putredinis). This provides potential insights into gut microbial pathway transcription that can vary over time, inducing phenotypical changes that are complementary to those linked to metagenomic abundances. The study's results highlight the strength of analysing both the activity and the presence of gut microorganisms to provide insight into the role of the microbiome in IBD.

@article {pmid29311644,
year = {2018},
author = {Schirmer, M and Franzosa, EA and Lloyd-Price, J and McIver, LJ and Schwager, R and Poon, TW and Ananthakrishnan, AN and Andrews, E and Barron, G and Lake, K and Prasad, M and Sauk, J and Stevens, B and Wilson, RG and Braun, J and Denson, LA and Kugathasan, S and McGovern, DPB and Vlamakis, H and Xavier, RJ and Huttenhower, C},
title = {Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.},
journal = {Nature microbiology},
volume = {3},
number = {3},
pages = {337-346},
doi = {10.1038/s41564-017-0089-z},
pmid = {29311644},
issn = {2058-5276},
support = {U54 DK102557/DK/NIDDK NIH HHS/United States ; U01 DK062413/DK/NIDDK NIH HHS/United States ; P30 DK043351/DK/NIDDK NIH HHS/United States ; R01 DK092405/DK/NIDDK NIH HHS/United States ; P01 DK046763/DK/NIDDK NIH HHS/United States ; UL1 TR001881/TR/NCATS NIH HHS/United States ; },
abstract = {Inflammatory bowel disease (IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. Genetic, environmental and microbial factors have been implicated in the onset and exacerbation of IBD. However, the mechanisms associating gut microbial dysbioses and aberrant immune responses remain largely unknown. The integrative Human Microbiome Project seeks to close these gaps by examining the dynamics of microbiome functionality in disease by profiling the gut microbiomes of >100 individuals sampled over a 1-year period. Here, we present the first results based on 78 paired faecal metagenomes and metatranscriptomes, and 222 additional metagenomes from 59 patients with Crohn's disease, 34 with ulcerative colitis and 24 non-IBD control patients. We demonstrate several cases in which measures of microbial gene expression in the inflamed gut can be informative relative to metagenomic profiles of functional potential. First, although many microbial organisms exhibited concordant DNA and RNA abundances, we also detected species-specific biases in transcriptional activity, revealing predominant transcription of pathways by individual microorganisms per host (for example, by Faecalibacterium prausnitzii). Thus, a loss of these organisms in disease may have more far-reaching consequences than suggested by their genomic abundances. Furthermore, we identified organisms that were metagenomically abundant but inactive or dormant in the gut with little or no expression (for example, Dialister invisus). Last, certain disease-specific microbial characteristics were more pronounced or only detectable at the transcript level, such as pathways that were predominantly expressed by different organisms in patients with IBD (for example, Bacteroides vulgatus and Alistipes putredinis). This provides potential insights into gut microbial pathway transcription that can vary over time, inducing phenotypical changes that are complementary to those linked to metagenomic abundances. The study's results highlight the strength of analysing both the activity and the presence of gut microorganisms to provide insight into the role of the microbiome in IBD.},
}

Due to the recent advances in high-throughput sequencing technologies, it becomes possible to directly analyze microbial communities in human body and environment. To understand how microbial communities adapt, develop, and interact with the human body and the surrounding environment, one of the fundamental challenges is to infer the interactions among different microbes. However, due to the compositional and high-dimensional nature of microbial data, statistical inference cannot offer reliable results. Consequently, new approaches that can accurately and robustly estimate the associations (putative interactions) among microbes are needed to analyze such compositional and high-dimensional data. We propose a novel framework called Microbial Prior Lasso (MPLasso) which integrates graph learning algorithm with microbial co-occurrences and associations obtained from scientific literature by using automated text mining. We show that MPLasso outperforms existing models in terms of accuracy, microbial network recovery rate, and reproducibility. Furthermore, the association networks we obtain from the Human Microbiome Project datasets show credible results when compared against laboratory data.

Thanks in large part to newer, better, and cheaper DNA sequencing technologies, an enormous number of metagenomic sequence datasets have been and continue to be generated, covering a huge variety of environmental niches, including several different human body sites. Comparing these metagenomes and identifying their commonalities and differences is a challenging task, due not only to the large amounts of data, but also because there are several methodological considerations that need to be taken into account to ensure an appropriate and sound comparison between datasets. In this chapter, we describe current techniques aimed at comparing metagenomes generated by 16S ribosomal RNA and shotgun DNA sequencing, emphasizing methodological issues that arise in these comparative studies. We provide a detailed case study to illustrate some of these techniques using data from the Human Microbiome Project comparing the microbial communities from ten buccal mucosa samples with ten tongue dorsum samples in terms of alpha diversity, beta diversity, and their taxonomic and functional profiles.

@article {pmid29277868,
year = {2018},
author = {Maltez Thomas, A and Prata Lima, F and Maria Silva Moura, L and Maria da Silva, A and Dias-Neto, E and Setubal, JC},
title = {Comparative Metagenomics.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {1704},
number = {},
pages = {243-260},
doi = {10.1007/978-1-4939-7463-4_8},
pmid = {29277868},
issn = {1940-6029},
abstract = {Thanks in large part to newer, better, and cheaper DNA sequencing technologies, an enormous number of metagenomic sequence datasets have been and continue to be generated, covering a huge variety of environmental niches, including several different human body sites. Comparing these metagenomes and identifying their commonalities and differences is a challenging task, due not only to the large amounts of data, but also because there are several methodological considerations that need to be taken into account to ensure an appropriate and sound comparison between datasets. In this chapter, we describe current techniques aimed at comparing metagenomes generated by 16S ribosomal RNA and shotgun DNA sequencing, emphasizing methodological issues that arise in these comparative studies. We provide a detailed case study to illustrate some of these techniques using data from the Human Microbiome Project comparing the microbial communities from ten buccal mucosa samples with ten tongue dorsum samples in terms of alpha diversity, beta diversity, and their taxonomic and functional profiles.},
}

RevDate: 2018-05-06

Huang L, Krüger J, A Sczyrba (2018)

Analyzing large scale genomic data on the cloud with Sparkhit.

Bioinformatics (Oxford, England), 34(9):1457-1465.

Motivation: The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform.

Results: Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92-157 times faster than MetaSpark on metagenomic fragment recruitment and 18-32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data.

Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/.

Contact: asczyrba@cebitec.uni-bielefeld.de.

Supplementary information: Supplementary data are available at Bioinformatics online.

@article {pmid29253074,
year = {2018},
author = {Huang, L and Krüger, J and Sczyrba, A},
title = {Analyzing large scale genomic data on the cloud with Sparkhit.},
journal = {Bioinformatics (Oxford, England)},
volume = {34},
number = {9},
pages = {1457-1465},
doi = {10.1093/bioinformatics/btx808},
pmid = {29253074},
issn = {1367-4811},
abstract = {Motivation: The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform.

Results: Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92-157 times faster than MetaSpark on metagenomic fragment recruitment and 18-32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data.

Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/.

Contact: asczyrba@cebitec.uni-bielefeld.de.

Supplementary information: Supplementary data are available at Bioinformatics online.},
}

Immune defence against pathogenic agents comprises the basic premise for the administration of vaccines. Vaccinations have hence prevented millions of infectious illnesses, hospitalizations and mortality. Acquired immunity comprises antibody and cell mediated responses and is characterized by its specificity and memory. Along a similar congruent yet diverse mode of disease prevention, the human host has negotiated from in utero and at birth with the intestinal commensal bacterial cohort to maintain local homeostasis in order to achieve immunological tolerance in the new born. The advent of the Human Microbiome Project has redefined an appreciation of the interactions between the host and bacteria in the intestines from one of a collection of toxic waste to one of a symbiotic existence. Probiotics comprise bacterial genera thought to provide a health benefit to the host. The intestinal microbiota has profound effects on local and extra-intestinal end organ physiology. As such, we further posit that the adjuvant administration of dedicated probiotic formulations can encourage the intestinal commensal cohort to beneficially participate in the intestinal microbiome-intestinal epithelia-innate-cell mediated immunity axes and cell mediated cellular immunity with vaccines aimed at preventing infectious diseases whilst conserving immunological tolerance. The strength of evidence for the positive effect of probiotic administration on acquired immune responses has come from various studies with viral and bacterial vaccines. We posit that the introduction early of probiotics may provide significant beneficial immune outcomes in neonates prior to commencing a vaccination schedule or in elderly adults prior to the administration of vaccinations against influenza viruses.

@article {pmid29232932,
year = {2017},
author = {Vitetta, L and Saltzman, ET and Thomsen, M and Nikov, T and Hall, S},
title = {Adjuvant Probiotics and the Intestinal Microbiome: Enhancing Vaccines and Immunotherapy Outcomes.},
journal = {Vaccines},
volume = {5},
number = {4},
pages = {},
doi = {10.3390/vaccines5040050},
pmid = {29232932},
issn = {2076-393X},
abstract = {Immune defence against pathogenic agents comprises the basic premise for the administration of vaccines. Vaccinations have hence prevented millions of infectious illnesses, hospitalizations and mortality. Acquired immunity comprises antibody and cell mediated responses and is characterized by its specificity and memory. Along a similar congruent yet diverse mode of disease prevention, the human host has negotiated from in utero and at birth with the intestinal commensal bacterial cohort to maintain local homeostasis in order to achieve immunological tolerance in the new born. The advent of the Human Microbiome Project has redefined an appreciation of the interactions between the host and bacteria in the intestines from one of a collection of toxic waste to one of a symbiotic existence. Probiotics comprise bacterial genera thought to provide a health benefit to the host. The intestinal microbiota has profound effects on local and extra-intestinal end organ physiology. As such, we further posit that the adjuvant administration of dedicated probiotic formulations can encourage the intestinal commensal cohort to beneficially participate in the intestinal microbiome-intestinal epithelia-innate-cell mediated immunity axes and cell mediated cellular immunity with vaccines aimed at preventing infectious diseases whilst conserving immunological tolerance. The strength of evidence for the positive effect of probiotic administration on acquired immune responses has come from various studies with viral and bacterial vaccines. We posit that the introduction early of probiotics may provide significant beneficial immune outcomes in neonates prior to commencing a vaccination schedule or in elderly adults prior to the administration of vaccinations against influenza viruses.},
}

RevDate: 2018-07-24CmpDate: 2018-07-24

Moffatt MF, WO Cookson (2017)

The lung microbiome in health and disease.

Clinical medicine (London, England), 17(6):525-529.

The Human Microbiome Project began 10 years ago, leading to a significant growth in understanding of the role the human microbiome plays in health and disease. In this article, we explain with an emphasis on the lung, the origins of microbiome research. We discuss how 16S rRNA gene sequencing became the first major molecular tool to examine the bacterial communities present within the human body. We highlight the pitfalls of molecular-based studies, such as false findings resulting from contamination, and the limitations of 16S rRNA gene sequencing. Knowledge about the lung microbiome has evolved from initial scepticism to the realisation that it might have a significant influence on many illnesses. We also discuss the lung microbiome in the context of disease by giving examples of important respiratory conditions. In addition, we draw attention to the challenges for metagenomic studies of respiratory samples and the importance of systematic bacterial isolation to enable host-microbiome interactions to be understood. We conclude by discussing how knowledge of the lung microbiome impacts current clinical diagnostics.

@article {pmid29196353,
year = {2017},
author = {Moffatt, MF and Cookson, WO},
title = {The lung microbiome in health and disease.},
journal = {Clinical medicine (London, England)},
volume = {17},
number = {6},
pages = {525-529},
doi = {10.7861/clinmedicine.17-6-525},
pmid = {29196353},
issn = {1473-4893},
mesh = {Asthma/microbiology ; Bronchiectasis/microbiology ; Cystic Fibrosis/microbiology ; Humans ; Lung/*microbiology ; Lung Diseases/*microbiology ; Metagenomics ; *Microbiota/genetics ; Pulmonary Disease, Chronic Obstructive/microbiology ; RNA, Ribosomal, 16S/genetics ; },
abstract = {The Human Microbiome Project began 10 years ago, leading to a significant growth in understanding of the role the human microbiome plays in health and disease. In this article, we explain with an emphasis on the lung, the origins of microbiome research. We discuss how 16S rRNA gene sequencing became the first major molecular tool to examine the bacterial communities present within the human body. We highlight the pitfalls of molecular-based studies, such as false findings resulting from contamination, and the limitations of 16S rRNA gene sequencing. Knowledge about the lung microbiome has evolved from initial scepticism to the realisation that it might have a significant influence on many illnesses. We also discuss the lung microbiome in the context of disease by giving examples of important respiratory conditions. In addition, we draw attention to the challenges for metagenomic studies of respiratory samples and the importance of systematic bacterial isolation to enable host-microbiome interactions to be understood. We conclude by discussing how knowledge of the lung microbiome impacts current clinical diagnostics.},
}

BACKGROUND: Most studies describing the human gut microbiome in healthy and diseased states have emphasized the bacterial component, but the fungal microbiome (i.e., the mycobiome) is beginning to gain recognition as a fundamental part of our microbiome. To date, human gut mycobiome studies have primarily been disease centric or in small cohorts of healthy individuals. To contribute to existing knowledge of the human mycobiome, we investigated the gut mycobiome of the Human Microbiome Project (HMP) cohort by sequencing the Internal Transcribed Spacer 2 (ITS2) region as well as the 18S rRNA gene.

RESULTS: Three hundred seventeen HMP stool samples were analyzed by ITS2 sequencing. Fecal fungal diversity was significantly lower in comparison to bacterial diversity. Yeast dominated the samples, comprising eight of the top 15 most abundant genera. Specifically, fungal communities were characterized by a high prevalence of Saccharomyces, Malassezia, and Candida, with S. cerevisiae, M. restricta, and C. albicans operational taxonomic units (OTUs) present in 96.8, 88.3, and 80.8% of samples, respectively. There was a high degree of inter- and intra-volunteer variability in fungal communities. However, S. cerevisiae, M. restricta, and C. albicans OTUs were found in 92.2, 78.3, and 63.6% of volunteers, respectively, in all samples donated over an approximately 1-year period. Metagenomic and 18S rRNA gene sequencing data agreed with ITS2 results; however, ITS2 sequencing provided greater resolution of the relatively low abundance mycobiome constituents.

CONCLUSIONS: Compared to bacterial communities, the human gut mycobiome is low in diversity and dominated by yeast including Saccharomyces, Malassezia, and Candida. Both inter- and intra-volunteer variability in the HMP cohort were high, revealing that unlike bacterial communities, an individual's mycobiome is no more similar to itself over time than to another person's. Nonetheless, several fungal species persisted across a majority of samples, evidence that a core gut mycobiome may exist. ITS2 sequencing data provided greater resolution of the mycobiome membership compared to metagenomic and 18S rRNA gene sequencing data, suggesting that it is a more sensitive method for studying the mycobiome of stool samples.

RESULTS: Three hundred seventeen HMP stool samples were analyzed by ITS2 sequencing. Fecal fungal diversity was significantly lower in comparison to bacterial diversity. Yeast dominated the samples, comprising eight of the top 15 most abundant genera. Specifically, fungal communities were characterized by a high prevalence of Saccharomyces, Malassezia, and Candida, with S. cerevisiae, M. restricta, and C. albicans operational taxonomic units (OTUs) present in 96.8, 88.3, and 80.8% of samples, respectively. There was a high degree of inter- and intra-volunteer variability in fungal communities. However, S. cerevisiae, M. restricta, and C. albicans OTUs were found in 92.2, 78.3, and 63.6% of volunteers, respectively, in all samples donated over an approximately 1-year period. Metagenomic and 18S rRNA gene sequencing data agreed with ITS2 results; however, ITS2 sequencing provided greater resolution of the relatively low abundance mycobiome constituents.

CONCLUSIONS: Compared to bacterial communities, the human gut mycobiome is low in diversity and dominated by yeast including Saccharomyces, Malassezia, and Candida. Both inter- and intra-volunteer variability in the HMP cohort were high, revealing that unlike bacterial communities, an individual's mycobiome is no more similar to itself over time than to another person's. Nonetheless, several fungal species persisted across a majority of samples, evidence that a core gut mycobiome may exist. ITS2 sequencing data provided greater resolution of the mycobiome membership compared to metagenomic and 18S rRNA gene sequencing data, suggesting that it is a more sensitive method for studying the mycobiome of stool samples.},
}

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.

Cohabitation of microbial communities with the host enables the formation of a symbiotic relationship that maintains homeostasis in the gut and beyond. One prevailing model suggests that this relationship relies on the capacity of host cells and tissues to remain tolerant to the strong immune stimulation generated by the microbiota such as the activation of Toll-like receptor 4 (TLR4) pathways by lipopolysaccharide (LPS). Indeed, gut microbial LPS is thought to be one of the most potent activators of innate immune signaling and an important mediator of the microbiome's influence on host physiology. In this study, we performed computational and experimental analyses of healthy human fecal samples to examine the TLR4 signaling capacity of the gut microbiota. These analyses revealed that an immunoinhibitory activity of LPS, conserved across the members of the order Bacteroidales and derived from an underacylated structural feature, silences TLR4 signaling for the entire consortium of organisms inhabiting the human gut. Comparative analysis of metagenomic data from the Human Microbiome Project and healthy-donor samples indicates that immune silencing via LPS is a microbe-intrinsic feature in all healthy adults. These findings challenge the current belief that robust TLR4 signaling is a feature of the microbiome and demonstrate that microbiome-derived LPS has the ability to facilitate host tolerance of gut microbes. These findings have broad implications for how we model host-microbe interactions and for our understanding of microbiome-linked disease. IMPORTANCE While the ability for humans to host a complex microbial ecosystem is an essential property of life, the mechanisms allowing for immune tolerance of such a large microbial load are not completely understood and are currently the focus of intense research. This study shows that an important proinflammatory pathway that is commonly triggered by pathogenic bacteria upon interaction with the host is, in fact, actively repressed by the bacteria of the gut microbiome, supporting the idea that beneficial microbes themselves contribute to the immune tolerance in support of homeostasis. These findings are important for two reasons. First, many currently assume that proinflammatory signaling by lipopolysaccharide is a fundamental feature of the gut flora. This assumption influences greatly how host-microbiome interactions are theoretically modeled but also how they are experimentally studied, by using robust TLR signaling conditions to simulate commensals. Second, elucidation of the mechanisms that support host-microbe tolerance is key to the development of therapeutics for both intestinal and systemic inflammatory disorders.

@article {pmid29152585,
year = {2017},
author = {d'Hennezel, E and Abubucker, S and Murphy, LO and Cullen, TW},
title = {Total Lipopolysaccharide from the Human Gut Microbiome Silences Toll-Like Receptor Signaling.},
journal = {mSystems},
volume = {2},
number = {6},
pages = {},
doi = {10.1128/mSystems.00046-17},
pmid = {29152585},
issn = {2379-5077},
abstract = {Cohabitation of microbial communities with the host enables the formation of a symbiotic relationship that maintains homeostasis in the gut and beyond. One prevailing model suggests that this relationship relies on the capacity of host cells and tissues to remain tolerant to the strong immune stimulation generated by the microbiota such as the activation of Toll-like receptor 4 (TLR4) pathways by lipopolysaccharide (LPS). Indeed, gut microbial LPS is thought to be one of the most potent activators of innate immune signaling and an important mediator of the microbiome's influence on host physiology. In this study, we performed computational and experimental analyses of healthy human fecal samples to examine the TLR4 signaling capacity of the gut microbiota. These analyses revealed that an immunoinhibitory activity of LPS, conserved across the members of the order Bacteroidales and derived from an underacylated structural feature, silences TLR4 signaling for the entire consortium of organisms inhabiting the human gut. Comparative analysis of metagenomic data from the Human Microbiome Project and healthy-donor samples indicates that immune silencing via LPS is a microbe-intrinsic feature in all healthy adults. These findings challenge the current belief that robust TLR4 signaling is a feature of the microbiome and demonstrate that microbiome-derived LPS has the ability to facilitate host tolerance of gut microbes. These findings have broad implications for how we model host-microbe interactions and for our understanding of microbiome-linked disease. IMPORTANCE While the ability for humans to host a complex microbial ecosystem is an essential property of life, the mechanisms allowing for immune tolerance of such a large microbial load are not completely understood and are currently the focus of intense research. This study shows that an important proinflammatory pathway that is commonly triggered by pathogenic bacteria upon interaction with the host is, in fact, actively repressed by the bacteria of the gut microbiome, supporting the idea that beneficial microbes themselves contribute to the immune tolerance in support of homeostasis. These findings are important for two reasons. First, many currently assume that proinflammatory signaling by lipopolysaccharide is a fundamental feature of the gut flora. This assumption influences greatly how host-microbiome interactions are theoretically modeled but also how they are experimentally studied, by using robust TLR signaling conditions to simulate commensals. Second, elucidation of the mechanisms that support host-microbe tolerance is key to the development of therapeutics for both intestinal and systemic inflammatory disorders.},
}

RevDate: 2018-03-03CmpDate: 2017-12-12

Schwager E, Mallick H, Ventz S, et al (2017)

A Bayesian method for detecting pairwise associations in compositional data.

PLoS computational biology, 13(11):e1005852 pii:PCOMPBIOL-D-17-00827.

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.

@article {pmid29140991,
year = {2017},
author = {Schwager, E and Mallick, H and Ventz, S and Huttenhower, C},
title = {A Bayesian method for detecting pairwise associations in compositional data.},
journal = {PLoS computational biology},
volume = {13},
number = {11},
pages = {e1005852},
doi = {10.1371/journal.pcbi.1005852},
pmid = {29140991},
issn = {1553-7358},
support = {U54 DK102557/DK/NIDDK NIH HHS/United States ; },
mesh = {Algorithms ; *Bayes Theorem ; Computational Biology/*methods ; *Computer Simulation ; Ecology ; Humans ; Markov Chains ; Microbiota ; *Models, Biological ; Proteobacteria ; },
abstract = {Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.},
}

Phylogenic inference using alignment-free methods for applications in microbial community surveys using 16s rRNA gene.

PloS one, 12(11):e0187940 pii:PONE-D-17-12951.

The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference.

@article {pmid29136663,
year = {2017},
author = {Zhang, Y and Alekseyenko, AV},
title = {Phylogenic inference using alignment-free methods for applications in microbial community surveys using 16s rRNA gene.},
journal = {PloS one},
volume = {12},
number = {11},
pages = {e0187940},
doi = {10.1371/journal.pone.0187940},
pmid = {29136663},
issn = {1932-6203},
mesh = {Microbiota/*genetics ; *Phylogeny ; RNA, Ribosomal, 16S/*genetics ; },
abstract = {The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference.},
}

MeSH Terms:

show MeSH Terms

hide MeSH Terms

Microbiota/*genetics*PhylogenyRNA, Ribosomal, 16S/*genetics

RevDate: 2018-08-22CmpDate: 2018-08-22

Li J, Fu R, Yang Y, et al (2018)

A metagenomic approach to dissect the genetic composition of enterotypes in Han Chinese and two Muslim groups.

Systematic and applied microbiology, 41(1):1-12.

Distinct enterotypes have been observed in the human gut but little is known about the genetic basis of the microbiome. Moreover, it is not clear how many genetic differences exist between enterotypes within or between populations. In this study, both the 16S rRNA gene and the metagenomes of the gut microbiota were sequenced from 48 Han Chinese, 48 Kazaks, and 96 Uyghurs, and taxonomies were assigned after de novo assembly. Single nucleotide polymorphisms were also identified by referring to data from the Human Microbiome Project. Systematic analysis of the gut communities in terms of their abundance and genetic composition was also performed, together with a genome-wide association study of the host genomes. The gut microbiota of 192 subjects was clearly classified into two enterotypes (Bacteroides and Prevotella). Interestingly, both enterotypes showed a clear genetic differentiation in terms of their functional catalogue of genes, especially for genes involved in amino acid and carbohydrate metabolism. In addition, several differentiated genera and genes were found among the three populations. Notably, one human variant (rs878394) was identified that showed significant association with the abundance of Prevotella, which is linked to LYPLAL1, a gene associated with body fat distribution, the waist-hip ratio and insulin sensitivity. Taken together, considerable differentiation was observed in gut microbes between enterotypes and among populations that was reflected in both the taxonomic composition and the genetic makeup of their functional genes, which could have been influenced by a variety of factors, such as diet and host genetic variation.

@article {pmid29129355,
year = {2018},
author = {Li, J and Fu, R and Yang, Y and Horz, HP and Guan, Y and Lu, Y and Lou, H and Tian, L and Zheng, S and Liu, H and Shi, M and Tang, K and Wang, S and Xu, S},
title = {A metagenomic approach to dissect the genetic composition of enterotypes in Han Chinese and two Muslim groups.},
journal = {Systematic and applied microbiology},
volume = {41},
number = {1},
pages = {1-12},
doi = {10.1016/j.syapm.2017.09.006},
pmid = {29129355},
issn = {1618-0984},
mesh = {Asian Continental Ancestry Group ; Bacteria/*classification/*genetics ; Cluster Analysis ; DNA, Bacterial/chemistry/genetics ; DNA, Ribosomal/chemistry/genetics ; Ethnic Groups ; *Gastrointestinal Microbiome ; Genetic Association Studies ; Healthy Volunteers ; Humans ; Islam ; Lysophospholipase/genetics ; *Metagenomics ; *Microbiota ; Phylogeny ; Polymorphism, Single Nucleotide ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; },
abstract = {Distinct enterotypes have been observed in the human gut but little is known about the genetic basis of the microbiome. Moreover, it is not clear how many genetic differences exist between enterotypes within or between populations. In this study, both the 16S rRNA gene and the metagenomes of the gut microbiota were sequenced from 48 Han Chinese, 48 Kazaks, and 96 Uyghurs, and taxonomies were assigned after de novo assembly. Single nucleotide polymorphisms were also identified by referring to data from the Human Microbiome Project. Systematic analysis of the gut communities in terms of their abundance and genetic composition was also performed, together with a genome-wide association study of the host genomes. The gut microbiota of 192 subjects was clearly classified into two enterotypes (Bacteroides and Prevotella). Interestingly, both enterotypes showed a clear genetic differentiation in terms of their functional catalogue of genes, especially for genes involved in amino acid and carbohydrate metabolism. In addition, several differentiated genera and genes were found among the three populations. Notably, one human variant (rs878394) was identified that showed significant association with the abundance of Prevotella, which is linked to LYPLAL1, a gene associated with body fat distribution, the waist-hip ratio and insulin sensitivity. Taken together, considerable differentiation was observed in gut microbes between enterotypes and among populations that was reflected in both the taxonomic composition and the genetic makeup of their functional genes, which could have been influenced by a variety of factors, such as diet and host genetic variation.},
}

BACKGROUND: It is clear that specific intestinal bacteria are involved in the development of different premalignant conditions along the gastrointestinal tract. An analysis of the microbial constituents in the context of pancreatic cystic lesions has, however, as yet not been performed. This consideration prompted us to explore whether endoscopically obtained pancreatic cyst fluids (PCF) contain bacterial DNA and to determine the genera of bacteria present in such material.

METHODS: Total DNA was isolated from 69 PCF samples. Bacterial 16S rRNA gene-specific PCR was performed followed by Sanger sequencing and de novo deep sequencing for the V3-V4 variable region of 16S rRNA gene.

RESULTS: We observed that 98.2% of the samples were positive in conventional PCR, and that 100% of selected PCF samples (n = 33) were positive for bacterial microbiota as determined by next generation sequencing (NGS). Comprehensive NGS data analysis of PCF showed the presence of 408 genera of bacteria, of which 17 bacterial genera were uniquely abundant to PCF, when compared to the Human Microbiome Project (HMP) database and 15 bacterial microbiota were uniquely abundant in HMP only. Bacteroides spp., Escherichia/Shigella spp., and Acidaminococcus spp. which were predominant in PCF, while also a substantial Staphylococcus spp. and Fusobacterium spp. component was detected.

CONCLUSION: These results reveal and characterize an apparently specific bacterial ecosystem in pancreatic cyst fluid samples and may reflect the local microbiota in the pancreas. Some taxa with potential deleterious functions are present in the bacterial abundance profiles, suggesting that the unique microbiome in this specific niche may contribute to neoplastic processes in the pancreas. Further studies are needed to explore the intricate relationship between pathophysiological status in the host pancreas and its microbiota.

METHODS: Total DNA was isolated from 69 PCF samples. Bacterial 16S rRNA gene-specific PCR was performed followed by Sanger sequencing and de novo deep sequencing for the V3-V4 variable region of 16S rRNA gene.

RESULTS: We observed that 98.2% of the samples were positive in conventional PCR, and that 100% of selected PCF samples (n = 33) were positive for bacterial microbiota as determined by next generation sequencing (NGS). Comprehensive NGS data analysis of PCF showed the presence of 408 genera of bacteria, of which 17 bacterial genera were uniquely abundant to PCF, when compared to the Human Microbiome Project (HMP) database and 15 bacterial microbiota were uniquely abundant in HMP only. Bacteroides spp., Escherichia/Shigella spp., and Acidaminococcus spp. which were predominant in PCF, while also a substantial Staphylococcus spp. and Fusobacterium spp. component was detected.

CONCLUSION: These results reveal and characterize an apparently specific bacterial ecosystem in pancreatic cyst fluid samples and may reflect the local microbiota in the pancreas. Some taxa with potential deleterious functions are present in the bacterial abundance profiles, suggesting that the unique microbiome in this specific niche may contribute to neoplastic processes in the pancreas. Further studies are needed to explore the intricate relationship between pathophysiological status in the host pancreas and its microbiota.},
}

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical 'strategy' approach. The current release of the database integrates microbial census data with sample details for nearly 14 000 samples originating from human, animal and environmental sources, including over 9000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

Lung diseases caused by microbial infections affect hundreds of millions of children and adults throughout the world. In Western populations, the treatment of lung infections is a primary driver of antibiotic resistance. Traditional therapeutic strategies have been based on the premise that the healthy lung is sterile and that infections grow in a pristine environment. As a consequence, rapid advances in our understanding of the composition of the microbiota of the skin and bowel have not yet been matched by studies of the respiratory tree. The recognition that the lungs are as populated with microorganisms as other mucosal surfaces provides the opportunity to reconsider the mechanisms and management of lung infections. Molecular analyses of the lung microbiota are revealing profound adverse responses to widespread antibiotic use, urbanization and globalization. This Opinion article proposes how technologies and concepts flowing from the Human Microbiome Project can transform the diagnosis and treatment of common lung diseases.

@article {pmid29062070,
year = {2018},
author = {Cookson, WOCM and Cox, MJ and Moffatt, MF},
title = {New opportunities for managing acute and chronic lung infections.},
journal = {Nature reviews. Microbiology},
volume = {16},
number = {2},
pages = {111-120},
doi = {10.1038/nrmicro.2017.122},
pmid = {29062070},
issn = {1740-1534},
support = {G1000758//Medical Research Council/United Kingdom ; },
abstract = {Lung diseases caused by microbial infections affect hundreds of millions of children and adults throughout the world. In Western populations, the treatment of lung infections is a primary driver of antibiotic resistance. Traditional therapeutic strategies have been based on the premise that the healthy lung is sterile and that infections grow in a pristine environment. As a consequence, rapid advances in our understanding of the composition of the microbiota of the skin and bowel have not yet been matched by studies of the respiratory tree. The recognition that the lungs are as populated with microorganisms as other mucosal surfaces provides the opportunity to reconsider the mechanisms and management of lung infections. Molecular analyses of the lung microbiota are revealing profound adverse responses to widespread antibiotic use, urbanization and globalization. This Opinion article proposes how technologies and concepts flowing from the Human Microbiome Project can transform the diagnosis and treatment of common lung diseases.},
}

RevDate: 2017-12-19

Ma ZS, D Ye (2017)

Trios-promising in silico biomarkers for differentiating the effect of disease on the human microbiome network.

Scientific reports, 7(1):13259 pii:10.1038/s41598-017-12959-3.

Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.

@article {pmid29038470,
year = {2017},
author = {Ma, ZS and Ye, D},
title = {Trios-promising in silico biomarkers for differentiating the effect of disease on the human microbiome network.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {13259},
doi = {10.1038/s41598-017-12959-3},
pmid = {29038470},
issn = {2045-2322},
abstract = {Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.},
}

Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results: The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

@article {pmid29028892,
year = {2018},
author = {Weber, N and Liou, D and Dommer, J and MacMenamin, P and Quiñones, M and Misner, I and Oler, AJ and Wan, J and Kim, L and Coakley McCarthy, M and Ezeji, S and Noble, K and Hurt, DE},
title = {Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis.},
journal = {Bioinformatics (Oxford, England)},
volume = {34},
number = {8},
pages = {1411-1413},
doi = {10.1093/bioinformatics/btx617},
pmid = {29028892},
issn = {1367-4811},
abstract = {Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results: The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research.

Microbiome, 5(1):132 pii:10.1186/s40168-017-0349-4.

The public commonly associates microorganisms with pathogens. This suspicion of microorganisms is understandable, as historically microorganisms have killed more humans than any other agent while remaining largely unknown until the late seventeenth century with the works of van Leeuwenhoek and Kircher. Despite our improved understanding regarding microorganisms, the general public are apt to think of diseases rather than of the majority of harmless or beneficial species that inhabit our bodies and the built and natural environment. As long as microbiome research was confined to labs, the public's exposure to microbiology was limited. The recent launch of global microbiome surveys, such as the Earth Microbiome Project and MetaSUB (Metagenomics and Metadesign of Subways and Urban Biomes) project, has raised ethical, financial, feasibility, and sustainability concerns as to the public's level of understanding and potential reaction to the findings, which, done improperly, risk negative implications for ongoing and future investigations, but done correctly, can facilitate a new vision of "smart cities." To facilitate improved future research, we describe here the major concerns that our discussions with ethics committees, community leaders, and government officials have raised, and we expound on how to address them. We further discuss ethical considerations of microbiome surveys and provide practical recommendations for public engagement.

@article {pmid28978331,
year = {2017},
author = {Shamarina, D and Stoyantcheva, I and Mason, CE and Bibby, K and Elhaik, E},
title = {Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research.},
journal = {Microbiome},
volume = {5},
number = {1},
pages = {132},
doi = {10.1186/s40168-017-0349-4},
pmid = {28978331},
issn = {2049-2618},
support = {R25 EB020393/EB/NIBIB NIH HHS/United States ; R01 ES021006/ES/NIEHS NIH HHS/United States ; },
mesh = {Environment Design ; *Ethics, Research ; Humans ; *Metagenome ; Metagenomics ; *Microbiota ; Public Opinion ; *Public Relations ; *Research ; },
abstract = {The public commonly associates microorganisms with pathogens. This suspicion of microorganisms is understandable, as historically microorganisms have killed more humans than any other agent while remaining largely unknown until the late seventeenth century with the works of van Leeuwenhoek and Kircher. Despite our improved understanding regarding microorganisms, the general public are apt to think of diseases rather than of the majority of harmless or beneficial species that inhabit our bodies and the built and natural environment. As long as microbiome research was confined to labs, the public's exposure to microbiology was limited. The recent launch of global microbiome surveys, such as the Earth Microbiome Project and MetaSUB (Metagenomics and Metadesign of Subways and Urban Biomes) project, has raised ethical, financial, feasibility, and sustainability concerns as to the public's level of understanding and potential reaction to the findings, which, done improperly, risk negative implications for ongoing and future investigations, but done correctly, can facilitate a new vision of "smart cities." To facilitate improved future research, we describe here the major concerns that our discussions with ethics committees, community leaders, and government officials have raised, and we expound on how to address them. We further discuss ethical considerations of microbiome surveys and provide practical recommendations for public engagement.},
}

Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. To integrate information from multiple views, data integration approaches such as nonnegative matrix factorization (NMF) have been developed to combine multiple heterogeneous data simultaneously to obtain a comprehensive representation. In this paper, we proposed a novel variant of symmetric nonnegative matrix factorization (SNMF), called Laplacian regularization based joint symmetric nonnegative matrix factorization (LJ-SNMF) for clustering multi-view data. We conduct extensive experiments on several realistic datasets including Human Microbiome Project data. The experimental results show that the proposed method outperforms other variants of NMF, which suggests the potential application of LJ-SNMF in clustering multi-view datasets. Additionally, we also demonstrate the capability of LJ-SNMF in community finding.

@article {pmid28961122,
year = {2017},
author = {Ma, Y and Hu, X and He, T and Jiang, X},
title = {Clustering and Integrating of Heterogeneous Microbiome Data by Joint Symmetric Nonnegative Matrix Factorization with Laplacian Regularization.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {},
number = {},
pages = {},
doi = {10.1109/TCBB.2017.2756628},
pmid = {28961122},
issn = {1557-9964},
abstract = {Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. To integrate information from multiple views, data integration approaches such as nonnegative matrix factorization (NMF) have been developed to combine multiple heterogeneous data simultaneously to obtain a comprehensive representation. In this paper, we proposed a novel variant of symmetric nonnegative matrix factorization (SNMF), called Laplacian regularization based joint symmetric nonnegative matrix factorization (LJ-SNMF) for clustering multi-view data. We conduct extensive experiments on several realistic datasets including Human Microbiome Project data. The experimental results show that the proposed method outperforms other variants of NMF, which suggests the potential application of LJ-SNMF in clustering multi-view datasets. Additionally, we also demonstrate the capability of LJ-SNMF in community finding.},
}

RevDate: 2018-03-02CmpDate: 2017-10-26

Lloyd-Price J, Mahurkar A, Rahnavard G, et al (2017)

Strains, functions and dynamics in the expanded Human Microbiome Project.

Nature, 550(7674):61-66.

The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, diversity, biogeography, and molecular function. The National Institutes of Health Human Microbiome Project has provided one of the broadest such characterizations so far. Here we introduce a second wave of data from the study, comprising 1,631 new metagenomes (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to provide new characterizations of microbiome personalization. Strain identification revealed subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity and enables an understanding of personalized microbiome function and dynamics.

BACKGROUND: Urbanization is associated with an increased risk for a number of diseases, including obesity, diabetes, and cancer, which all also show associations with the microbiome. While microbial community composition has been shown to vary across continents and in traditional versus Westernized societies, few studies have examined urban-rural differences in neighboring communities within a single country undergoing rapid urbanization. In this study, we compared the gut microbiome, plasma metabolome, dietary habits, and health biomarkers of rural and urban people from a single Chinese province.

RESULTS: We identified significant differences in the microbiota and microbiota-related plasma metabolites in rural versus recently urban subjects from the Hunan province of China. Microbes with higher relative abundance in Chinese urban samples have been associated with disease in other studies and were substantially more prevalent in the Human Microbiome Project cohort of American subjects. Furthermore, using whole metagenome sequencing, we found that urbanization was associated with a loss of microbial diversity and changes in the relative abundances of Viruses, Archaea, and Bacteria. Gene diversity, however, increased with urbanization, along with the proportion of reads associated with antibiotic resistance and virulence, which were strongly correlated with the presence of Escherichia and Shigella.

CONCLUSIONS: Our data suggest that urbanization has produced convergent evolution of the gut microbial composition in American and urban Chinese populations, resulting in similar compositional patterns of abundant microbes through similar lifestyles on different continents, including a loss of potentially beneficial bacteria and an increase in potentially harmful genes via increased relative abundance of Escherichia and Shigella.

RESULTS: We identified significant differences in the microbiota and microbiota-related plasma metabolites in rural versus recently urban subjects from the Hunan province of China. Microbes with higher relative abundance in Chinese urban samples have been associated with disease in other studies and were substantially more prevalent in the Human Microbiome Project cohort of American subjects. Furthermore, using whole metagenome sequencing, we found that urbanization was associated with a loss of microbial diversity and changes in the relative abundances of Viruses, Archaea, and Bacteria. Gene diversity, however, increased with urbanization, along with the proportion of reads associated with antibiotic resistance and virulence, which were strongly correlated with the presence of Escherichia and Shigella.

CONCLUSIONS: Our data suggest that urbanization has produced convergent evolution of the gut microbial composition in American and urban Chinese populations, resulting in similar compositional patterns of abundant microbes through similar lifestyles on different continents, including a loss of potentially beneficial bacteria and an increase in potentially harmful genes via increased relative abundance of Escherichia and Shigella.},
}

BACKGROUND: Bacterial vaginosis (BV) is the leading dysbiosis of the vaginal microbiome. The pathways leading towards the development of BV are not well understood. Gardnerella vaginalis is frequently associated with BV. G. vaginalis produces the cholesterol-dependent cytolysin (CDC), vaginolysin, which can lyse a variety of human cells and is thought to play a role in pathogenesis. Because membrane cholesterol is required for vaginolysin to function, and because HMG-CoA reductase inhibitors (statins) affect not only serum levels of cholesterol but membrane levels as well, we hypothesized that statins might affect the vaginal microbiome.

METHODS: To investigate the relationship between use of the statins and the vaginal microbiome, we analyzed 16S rRNA gene taxonomic surveys performed on vaginal samples from 133 women who participated in the Vaginal Human Microbiome Project and who were taking statins at the time of sampling, 152 women who reported high cholesterol levels but were not taking statins, and 316 women who did not report high cholesterol. To examine the effect of statins on the cytolytic effect of vaginolysin, the cholesterol-dependent cytolysin (CDC) produced by Gardnerella vaginalis, we assessed the effect of simvastatin pretreatment of VK2E6/E7 vaginal epithelial cells on vaginolysin-mediated cytotoxicity.

RESULTS: The mean proportion of G. vaginalis among women taking statins was significantly lower relative to women not using statins. Women using statins had higher mean proportions of Lactobacillus crispatus relative to women with normal cholesterol levels, and higher levels of Lactobacillus jensenii relative to women with high cholesterol but not taking statins. In vitro, vaginal epithelial cells pretreated with simvastatin were relatively resistant to vaginolysin and this effect was inhibited by cholesterol.

CONCLUSIONS: In this cross-sectional study, statin use was associated with reduced proportions of G. vaginalis and greater proportions of beneficial lactobacilli within the vaginal microbiome. The negative association between statin use and G. vaginalis may be related to inhibition of vaginolysin function.

METHODS: To investigate the relationship between use of the statins and the vaginal microbiome, we analyzed 16S rRNA gene taxonomic surveys performed on vaginal samples from 133 women who participated in the Vaginal Human Microbiome Project and who were taking statins at the time of sampling, 152 women who reported high cholesterol levels but were not taking statins, and 316 women who did not report high cholesterol. To examine the effect of statins on the cytolytic effect of vaginolysin, the cholesterol-dependent cytolysin (CDC) produced by Gardnerella vaginalis, we assessed the effect of simvastatin pretreatment of VK2E6/E7 vaginal epithelial cells on vaginolysin-mediated cytotoxicity.

RESULTS: The mean proportion of G. vaginalis among women taking statins was significantly lower relative to women not using statins. Women using statins had higher mean proportions of Lactobacillus crispatus relative to women with normal cholesterol levels, and higher levels of Lactobacillus jensenii relative to women with high cholesterol but not taking statins. In vitro, vaginal epithelial cells pretreated with simvastatin were relatively resistant to vaginolysin and this effect was inhibited by cholesterol.

CONCLUSIONS: In this cross-sectional study, statin use was associated with reduced proportions of G. vaginalis and greater proportions of beneficial lactobacilli within the vaginal microbiome. The negative association between statin use and G. vaginalis may be related to inhibition of vaginolysin function.},
}

PURPOSE OF REVIEW: Studies in microbiota-mediated health risks have gained traction in recent years since the compilation of the Human Microbiome Project. No longer do we believe that our gut microbiota is an inert set of microorganisms that reside in the body without consequence. In this review, we discuss the recent findings which further our understanding of the connection between the gut microbiota and the atherosclerosis.

RECENT FINDINGS: We evaluate studies which illustrate the current understanding of the relationship between infection, immunity, altered metabolism, and bacterial products such as immune activators or dietary metabolites and their contributions to the development of atherosclerosis. In particular, we critically examine rec ent clinical and mechanistic findings for the novel microbiota-dependent dietary metabolite, trimethylamine N-oxide (TMAO), which has been implicated in atherosclerosis. These discoveries are now becoming integrated with advances in microbiota profiling which enhance our ability to interrogate the functional role of the gut microbiome and develop strategies for targeted therapeutics. The gut microbiota is a multi-faceted system that is unraveling novel contributors to the development and progression of atherosclerosis. In this review, we discuss historic and novel contributors while highlighting the TMAO story mainly as an example of the various paths taken beyond deciphering microbial composition to elucidate downstream mechanisms that promote (or protect from) atherogenesis in the hopes of translating these findings from bench to bedside.

RECENT FINDINGS: We evaluate studies which illustrate the current understanding of the relationship between infection, immunity, altered metabolism, and bacterial products such as immune activators or dietary metabolites and their contributions to the development of atherosclerosis. In particular, we critically examine rec ent clinical and mechanistic findings for the novel microbiota-dependent dietary metabolite, trimethylamine N-oxide (TMAO), which has been implicated in atherosclerosis. These discoveries are now becoming integrated with advances in microbiota profiling which enhance our ability to interrogate the functional role of the gut microbiome and develop strategies for targeted therapeutics. The gut microbiota is a multi-faceted system that is unraveling novel contributors to the development and progression of atherosclerosis. In this review, we discuss historic and novel contributors while highlighting the TMAO story mainly as an example of the various paths taken beyond deciphering microbial composition to elucidate downstream mechanisms that promote (or protect from) atherogenesis in the hopes of translating these findings from bench to bedside.},
}

Disturbing the balance: effect of contact lens use on the ocular proteome and microbiome.

Clinical & experimental optometry, 100(5):459-472.

Contact lens wear is a popular, convenient and effective method for vision correction. In recent years, contact lens practice has expanded to include new paradigms, including orthokeratology; however, their use is not entirely without risk, as the incidence of infection has consistently been reported to be higher in contact lens wearers. The explanations for this increased susceptibility have largely focused on physical damage, especially to the cornea, due to a combination of hypoxia, mechanical trauma, deposits and solution cytotoxicity, as well as poor compliance with care routines leading to introduction of pathogens into the ocular environment. However, in recent years, with the increasing availability and reduced cost of molecular techniques, the ocular environment has received greater attention with in-depth studies of proteins and other components. Numerous proteins were found to be present in the tears and their functions and interactions indicate that the tears are far more complex than formerly presumed. In addition, the concept of a sterile or limited microbial population on the ocular surface has been challenged by analysis of the microbiome. Ocular microbiome was not considered as one of the key sites for the Human Microbiome Project, as it was thought to be limited compared to other body sites. This was proven to be fallacious, as a wide variety of micro-organisms were identified in the analyses of human tears. Thus, the ocular environment is now recognised to be more complicated and interference with this ecological balance may lead to adverse effects. The use of contact lenses clearly changes the situation at the ocular surface, which may result in consequences which disturb the balance in the healthy eye.

@article {pmid28771841,
year = {2017},
author = {Boost, M and Cho, P and Wang, Z},
title = {Disturbing the balance: effect of contact lens use on the ocular proteome and microbiome.},
journal = {Clinical & experimental optometry},
volume = {100},
number = {5},
pages = {459-472},
doi = {10.1111/cxo.12582},
pmid = {28771841},
issn = {1444-0938},
mesh = {Contact Lenses/*utilization ; Cornea/*metabolism ; Humans ; Microbiota/*physiology ; Proteome/*metabolism ; Proteostasis/*physiology ; Refractive Errors/therapy ; Tears/*metabolism ; Vision Disorders/therapy ; },
abstract = {Contact lens wear is a popular, convenient and effective method for vision correction. In recent years, contact lens practice has expanded to include new paradigms, including orthokeratology; however, their use is not entirely without risk, as the incidence of infection has consistently been reported to be higher in contact lens wearers. The explanations for this increased susceptibility have largely focused on physical damage, especially to the cornea, due to a combination of hypoxia, mechanical trauma, deposits and solution cytotoxicity, as well as poor compliance with care routines leading to introduction of pathogens into the ocular environment. However, in recent years, with the increasing availability and reduced cost of molecular techniques, the ocular environment has received greater attention with in-depth studies of proteins and other components. Numerous proteins were found to be present in the tears and their functions and interactions indicate that the tears are far more complex than formerly presumed. In addition, the concept of a sterile or limited microbial population on the ocular surface has been challenged by analysis of the microbiome. Ocular microbiome was not considered as one of the key sites for the Human Microbiome Project, as it was thought to be limited compared to other body sites. This was proven to be fallacious, as a wide variety of micro-organisms were identified in the analyses of human tears. Thus, the ocular environment is now recognised to be more complicated and interference with this ecological balance may lead to adverse effects. The use of contact lenses clearly changes the situation at the ocular surface, which may result in consequences which disturb the balance in the healthy eye.},
}

A Stoichioproteomic Analysis of Samples from the Human Microbiome Project.

Frontiers in microbiology, 8:1119.

Ecological stoichiometry (ES) uses organism-specific elemental content to explain differences in species life histories, species interactions, community organization, environmental constraints and even ecosystem function. Although ES has been successfully applied to a range of different organisms, most emphasis on microbial ecological stoichiometry focuses on lake, ocean, and soil communities. With the recent advances in human microbiome research, however, large amounts of data are being generated that describe differences in community composition across body sites and individuals. We suggest that ES may provide a framework for beginning to understand the structure, organization, and function of human microbial communities, including why certain organisms exist at certain locations, and how they interact with both the other microbes in their environment and their human host. As a first step, we undertake a stoichioproteomic analysis of microbial communities from different body sites. Specifically, we compare and contrast the elemental composition of microbial protein samples using annotated sequencing data from 690 gut, vaginal, oral, nares, and skin samples currently available through the Human Microbiome Project. Our results suggest significant differences in both the median and variance of the carbon, oxygen, nitrogen, and sulfur contents of microbial protein samples from different locations. For example, whereas proteins from vaginal sites are high in carbon, proteins from skin and nasal sites are high in nitrogen and oxygen. Meanwhile, proteins from stool (the gut) are particularly high in sulfur content. We interpret these differences in terms of the local environments at different human body sites, including atmospheric exposure and food intake rates.

@article {pmid28769875,
year = {2017},
author = {Vecchio-Pagan, B and Bewick, S and Mainali, K and Karig, DK and Fagan, WF},
title = {A Stoichioproteomic Analysis of Samples from the Human Microbiome Project.},
journal = {Frontiers in microbiology},
volume = {8},
number = {},
pages = {1119},
doi = {10.3389/fmicb.2017.01119},
pmid = {28769875},
issn = {1664-302X},
abstract = {Ecological stoichiometry (ES) uses organism-specific elemental content to explain differences in species life histories, species interactions, community organization, environmental constraints and even ecosystem function. Although ES has been successfully applied to a range of different organisms, most emphasis on microbial ecological stoichiometry focuses on lake, ocean, and soil communities. With the recent advances in human microbiome research, however, large amounts of data are being generated that describe differences in community composition across body sites and individuals. We suggest that ES may provide a framework for beginning to understand the structure, organization, and function of human microbial communities, including why certain organisms exist at certain locations, and how they interact with both the other microbes in their environment and their human host. As a first step, we undertake a stoichioproteomic analysis of microbial communities from different body sites. Specifically, we compare and contrast the elemental composition of microbial protein samples using annotated sequencing data from 690 gut, vaginal, oral, nares, and skin samples currently available through the Human Microbiome Project. Our results suggest significant differences in both the median and variance of the carbon, oxygen, nitrogen, and sulfur contents of microbial protein samples from different locations. For example, whereas proteins from vaginal sites are high in carbon, proteins from skin and nasal sites are high in nitrogen and oxygen. Meanwhile, proteins from stool (the gut) are particularly high in sulfur content. We interpret these differences in terms of the local environments at different human body sites, including atmospheric exposure and food intake rates.},
}

RevDate: 2018-03-28

Dheilly NM, Bolnick D, Bordenstein S, et al (2017)

Parasite Microbiome Project: Systematic Investigation of Microbiome Dynamics within and across Parasite-Host Interactions.

mSystems, 2(4): pii:mSystems00050-17.

Understanding how microbiomes affect host resistance, parasite virulence, and parasite-associated diseases requires a collaborative effort between parasitologists, microbial ecologists, virologists, and immunologists. We hereby propose the Parasite Microbiome Project to bring together researchers with complementary expertise and to study the role of microbes in host-parasite interactions. Data from the Parasite Microbiome Project will help identify the mechanisms driving microbiome variation in parasites and infected hosts and how that variation is associated with the ecology and evolution of parasites and their disease outcomes. This is a call to arms to prevent fragmented research endeavors, encourage best practices in experimental approaches, and allow reliable comparative analyses across model systems. It is also an invitation to foundations and national funding agencies to propel the field of parasitology into the microbiome/metagenomic era.

@article {pmid28761932,
year = {2017},
author = {Dheilly, NM and Bolnick, D and Bordenstein, S and Brindley, PJ and Figuères, C and Holmes, EC and Martínez Martínez, J and Phillips, AJ and Poulin, R and Rosario, K},
title = {Parasite Microbiome Project: Systematic Investigation of Microbiome Dynamics within and across Parasite-Host Interactions.},
journal = {mSystems},
volume = {2},
number = {4},
pages = {},
doi = {10.1128/mSystems.00050-17},
pmid = {28761932},
issn = {2379-5077},
support = {P30 DK058404/DK/NIDDK NIH HHS/United States ; },
abstract = {Understanding how microbiomes affect host resistance, parasite virulence, and parasite-associated diseases requires a collaborative effort between parasitologists, microbial ecologists, virologists, and immunologists. We hereby propose the Parasite Microbiome Project to bring together researchers with complementary expertise and to study the role of microbes in host-parasite interactions. Data from the Parasite Microbiome Project will help identify the mechanisms driving microbiome variation in parasites and infected hosts and how that variation is associated with the ecology and evolution of parasites and their disease outcomes. This is a call to arms to prevent fragmented research endeavors, encourage best practices in experimental approaches, and allow reliable comparative analyses across model systems. It is also an invitation to foundations and national funding agencies to propel the field of parasitology into the microbiome/metagenomic era.},
}

Helicobacter pylori (Hp) is the primary cause of gastric cancer but we know little of its relative abundance and other microbes in the stomach, especially at the time of gastric cancer diagnosis. Here we characterized the taxonomic and derived functional profiles of gastric microbiota in two different sets of gastric cancer patients, and compared them with microbial profiles in other body sites. Paired non-malignant and tumor tissues were sampled from 160 gastric cancer patients with 80 from China and 80 from Mexico. The 16S rRNA gene V3-V4 region was sequenced using MiSeq platform for taxonomic profiles. PICRUSt was used to predict functional profiles. Human Microbiome Project was used for comparison. We showed that Hp is the most abundant member of gastric microbiota in both Chinese and Mexican samples (51 and 24%, respectively), followed by oral-associated bacteria. Taxonomic (phylum-level) profiles of stomach microbiota resembled oral microbiota, especially when the Helicobacter reads were removed. The functional profiles of stomach microbiota, however, were distinct from those found in other body sites and had higher inter-subject dissimilarity. Gastric microbiota composition did not differ by Hp colonization status or stomach anatomic sites, but did differ between paired non-malignant and tumor tissues in either Chinese or Mexican samples. Our study showed that Hp is the dominant member of the non-malignant gastric tissue microbiota in many gastric cancer patients. Our results provide insights on the gastric microbiota composition and function in gastric cancer patients, which may have important clinical implications.

@article {pmid28730144,
year = {2017},
author = {Yu, G and Torres, J and Hu, N and Medrano-Guzman, R and Herrera-Goepfert, R and Humphrys, MS and Wang, L and Wang, C and Ding, T and Ravel, J and Taylor, PR and Abnet, CC and Goldstein, AM},
title = {Molecular Characterization of the Human Stomach Microbiota in Gastric Cancer Patients.},
journal = {Frontiers in cellular and infection microbiology},
volume = {7},
number = {},
pages = {302},
doi = {10.3389/fcimb.2017.00302},
pmid = {28730144},
issn = {2235-2988},
mesh = {Adult ; Aged ; Bacteria/classification/genetics/*isolation & purification ; China ; Female ; *Gastrointestinal Microbiome ; Helicobacter pylori/classification/genetics/isolation & purification ; Humans ; Male ; Mexico ; Middle Aged ; Stomach/*microbiology ; Stomach Neoplasms/*microbiology ; Young Adult ; },
abstract = {Helicobacter pylori (Hp) is the primary cause of gastric cancer but we know little of its relative abundance and other microbes in the stomach, especially at the time of gastric cancer diagnosis. Here we characterized the taxonomic and derived functional profiles of gastric microbiota in two different sets of gastric cancer patients, and compared them with microbial profiles in other body sites. Paired non-malignant and tumor tissues were sampled from 160 gastric cancer patients with 80 from China and 80 from Mexico. The 16S rRNA gene V3-V4 region was sequenced using MiSeq platform for taxonomic profiles. PICRUSt was used to predict functional profiles. Human Microbiome Project was used for comparison. We showed that Hp is the most abundant member of gastric microbiota in both Chinese and Mexican samples (51 and 24%, respectively), followed by oral-associated bacteria. Taxonomic (phylum-level) profiles of stomach microbiota resembled oral microbiota, especially when the Helicobacter reads were removed. The functional profiles of stomach microbiota, however, were distinct from those found in other body sites and had higher inter-subject dissimilarity. Gastric microbiota composition did not differ by Hp colonization status or stomach anatomic sites, but did differ between paired non-malignant and tumor tissues in either Chinese or Mexican samples. Our study showed that Hp is the dominant member of the non-malignant gastric tissue microbiota in many gastric cancer patients. Our results provide insights on the gastric microbiota composition and function in gastric cancer patients, which may have important clinical implications.},
}

Microbiome-encoded β-glucuronidase (GUS) enzymes play important roles in human health by metabolizing drugs in the gastrointestinal (GI) tract. The numbers, types, and diversity of these proteins in the human GI microbiome, however, remain undefined. We present an atlas of GUS enzymes comprehensive for the Human Microbiome Project GI database. We identify 3,013 total and 279 unique microbiome-encoded GUS proteins clustered into six unique structural categories. We assign their taxonomy, assess cellular localization, reveal the inter-individual variability within the 139 individuals sampled, and discover 112 novel microbial GUS enzymes. A representative in vitro panel of the most common GUS proteins by read abundances highlights structural and functional variabilities within the family, including their differential processing of smaller glucuronides and larger carbohydrates. These data provide a sequencing-to-molecular roadmap for examining microbiome-encoded enzymes essential to human health.

The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered "HMA indicators" and "LMA indicators." Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n = 44) and LMA (n = 74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.

@article {pmid28533766,
year = {2017},
author = {Moitinho-Silva, L and Steinert, G and Nielsen, S and Hardoim, CCP and Wu, YC and McCormack, GP and López-Legentil, S and Marchant, R and Webster, N and Thomas, T and Hentschel, U},
title = {Predicting the HMA-LMA Status in Marine Sponges by Machine Learning.},
journal = {Frontiers in microbiology},
volume = {8},
number = {},
pages = {752},
doi = {10.3389/fmicb.2017.00752},
pmid = {28533766},
issn = {1664-302X},
abstract = {The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered "HMA indicators" and "LMA indicators." Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n = 44) and LMA (n = 74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.},
}

RevDate: 2017-12-15CmpDate: 2017-09-20

Rath S, Heidrich B, Pieper DH, et al (2017)

Uncovering the trimethylamine-producing bacteria of the human gut microbiota.

Microbiome, 5(1):54 pii:10.1186/s40168-017-0271-9.

BACKGROUND: Trimethylamine (TMA), produced by the gut microbiota from dietary quaternary amines (mainly choline and carnitine), is associated with atherosclerosis and severe cardiovascular disease. Currently, little information on the composition of TMA producers in the gut is available due to their low abundance and the requirement of specific functional-based detection methods as many taxa show disparate abilities to produce that compound.

RESULTS: In order to examine the TMA-forming potential of microbial communities, we established databases for the key genes of the main TMA-synthesis pathways, encoding choline TMA-lyase (cutC) and carnitine oxygenase (cntA), using a multi-level screening approach on 67,134 genomes revealing 1107 and 6738 candidates to exhibit cutC and cntA, respectively. Gene-targeted assays enumerating the TMA-producing community by quantitative PCR and characterizing its composition via Illumina sequencing were developed and applied on human fecal samples (n = 50) where all samples contained potential TMA producers (cutC was detected in all individuals, whereas only 26% harbored cntA) constituting, however, only a minor part of the total community (below 1% in most samples). Obtained cutC amplicons were associated with various taxa, in particular with Clostridium XIVa strains and Eubacterium sp. strain AB3007, though a bulk of sequences displayed low nucleotide identities to references (average 86% ± 7%) indicating that key human TMA producers are yet to be isolated. Co-occurrence analysis revealed specific groups governing the community structure of cutC-exhibiting taxa across samples. CntA amplicons displayed high identities (~99%) to Gammaproteobacteria-derived references, primarily from Escherichia coli. Metagenomic analysis of samples provided by the Human Microbiome Project (n = 154) confirmed the abundance patterns as well as overall taxonomic compositions obtained with our assays, though at much lower resolution, whereas 16S ribosomal RNA gene sequence analysis could not adequately uncover the TMA-producing potential.

CONCLUSIONS: In this study, we developed a diagnostic framework that enabled the quantification and comprehensive characterization of the TMA-producing potential in human fecal samples. The key players were identified, and together with predictions on their environmental niches using functional genomics on most closely related reference strains, we provide crucial information for the development of specific treatment strategies to restrain TMA producers and limit their proliferation.

RESULTS: In order to examine the TMA-forming potential of microbial communities, we established databases for the key genes of the main TMA-synthesis pathways, encoding choline TMA-lyase (cutC) and carnitine oxygenase (cntA), using a multi-level screening approach on 67,134 genomes revealing 1107 and 6738 candidates to exhibit cutC and cntA, respectively. Gene-targeted assays enumerating the TMA-producing community by quantitative PCR and characterizing its composition via Illumina sequencing were developed and applied on human fecal samples (n = 50) where all samples contained potential TMA producers (cutC was detected in all individuals, whereas only 26% harbored cntA) constituting, however, only a minor part of the total community (below 1% in most samples). Obtained cutC amplicons were associated with various taxa, in particular with Clostridium XIVa strains and Eubacterium sp. strain AB3007, though a bulk of sequences displayed low nucleotide identities to references (average 86% ± 7%) indicating that key human TMA producers are yet to be isolated. Co-occurrence analysis revealed specific groups governing the community structure of cutC-exhibiting taxa across samples. CntA amplicons displayed high identities (~99%) to Gammaproteobacteria-derived references, primarily from Escherichia coli. Metagenomic analysis of samples provided by the Human Microbiome Project (n = 154) confirmed the abundance patterns as well as overall taxonomic compositions obtained with our assays, though at much lower resolution, whereas 16S ribosomal RNA gene sequence analysis could not adequately uncover the TMA-producing potential.

CONCLUSIONS: In this study, we developed a diagnostic framework that enabled the quantification and comprehensive characterization of the TMA-producing potential in human fecal samples. The key players were identified, and together with predictions on their environmental niches using functional genomics on most closely related reference strains, we provide crucial information for the development of specific treatment strategies to restrain TMA producers and limit their proliferation.},
}

Insights of the dental calculi microbiome of pre-Columbian inhabitants from Puerto Rico.

PeerJ, 5:e3277 pii:3277.

BACKGROUND: The study of ancient microorganisms in mineralized dental plaque or calculi is providing insights into microbial evolution, as well as lifestyles and disease states of extinct cultures; yet, little is still known about the oral microbial community structure and function of pre-Columbian Caribbean cultures. In the present study, we investigated the dental calculi microbiome and predicted function of one of these cultures, known as the Saladoid. The Saladoids were horticulturalists that emphasized root-crop production. Fruits, as well as small marine and terrestrial animals were also part of the Saladoid diet.

METHODS: Dental calculi samples were recovered from the archaeological site of Sorcé, in the municipal island of Vieques, Puerto Rico, characterized using 16S rRNA gene high-throughput sequencing, and compared to the microbiome of previously characterized coprolites of the same culture, as well modern plaque, saliva and stool microbiomes available from the Human Microbiome Project.

RESULTS: Actinobacteria, Proteobacteria and Firmicutes comprised the majority of the Saladoid dental calculi microbiome. The Saladoid dental calculi microbiome was distinct when compared to those of modern saliva and dental plaque, but showed the presence of common inhabitants of modern oral cavities including Streptococcus sp., Veillonella dispar and Rothia mucilaginosa. Cell motility, signal transduction and biosynthesis of other secondary metabolites may be unique features of the Saladoid microbiome.

DISCUSSION: Results suggest that the Saladoid dental calculi microbiome structure and function may possibly reflect a horticulturalist lifestyle and distinct dietary habits. Results also open the opportunity to further elucidate oral disease states in extinct Caribbean cultures and extinct indigenous cultures with similar lifestyles.

@article {pmid28480145,
year = {2017},
author = {Santiago-Rodriguez, TM and Narganes-Storde, Y and Chanlatte-Baik, L and Toranzos, GA and Cano, RJ},
title = {Insights of the dental calculi microbiome of pre-Columbian inhabitants from Puerto Rico.},
journal = {PeerJ},
volume = {5},
number = {},
pages = {e3277},
doi = {10.7717/peerj.3277},
pmid = {28480145},
issn = {2167-8359},
abstract = {BACKGROUND: The study of ancient microorganisms in mineralized dental plaque or calculi is providing insights into microbial evolution, as well as lifestyles and disease states of extinct cultures; yet, little is still known about the oral microbial community structure and function of pre-Columbian Caribbean cultures. In the present study, we investigated the dental calculi microbiome and predicted function of one of these cultures, known as the Saladoid. The Saladoids were horticulturalists that emphasized root-crop production. Fruits, as well as small marine and terrestrial animals were also part of the Saladoid diet.

METHODS: Dental calculi samples were recovered from the archaeological site of Sorcé, in the municipal island of Vieques, Puerto Rico, characterized using 16S rRNA gene high-throughput sequencing, and compared to the microbiome of previously characterized coprolites of the same culture, as well modern plaque, saliva and stool microbiomes available from the Human Microbiome Project.

RESULTS: Actinobacteria, Proteobacteria and Firmicutes comprised the majority of the Saladoid dental calculi microbiome. The Saladoid dental calculi microbiome was distinct when compared to those of modern saliva and dental plaque, but showed the presence of common inhabitants of modern oral cavities including Streptococcus sp., Veillonella dispar and Rothia mucilaginosa. Cell motility, signal transduction and biosynthesis of other secondary metabolites may be unique features of the Saladoid microbiome.

DISCUSSION: Results suggest that the Saladoid dental calculi microbiome structure and function may possibly reflect a horticulturalist lifestyle and distinct dietary habits. Results also open the opportunity to further elucidate oral disease states in extinct Caribbean cultures and extinct indigenous cultures with similar lifestyles.},
}

BACKGROUND: Fecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection and shows promise for treating other medical conditions associated with intestinal dysbioses. However, we lack a sufficient understanding of which microbial populations successfully colonize the recipient gut, and the widely used approaches to study the microbial ecology of FMT experiments fail to provide enough resolution to identify populations that are likely responsible for FMT-derived benefits.

METHODS: We used shotgun metagenomics together with assembly and binning strategies to reconstruct metagenome-assembled genomes (MAGs) from fecal samples of a single FMT donor. We then used metagenomic mapping to track the occurrence and distribution patterns of donor MAGs in two FMT recipients.

RESULTS: Our analyses revealed that 22% of the 92 highly complete bacterial MAGs that we identified from the donor successfully colonized and remained abundant in two recipients for at least 8 weeks. Most MAGs with a high colonization rate belonged to the order Bacteroidales. The vast majority of those that lacked evidence of colonization belonged to the order Clostridiales, and colonization success was negatively correlated with the number of genes related to sporulation. Our analysis of 151 publicly available gut metagenomes showed that the donor MAGs that colonized both recipients were prevalent, and the ones that colonized neither were rare across the participants of the Human Microbiome Project. Although our dataset showed a link between taxonomy and the colonization ability of a given MAG, we also identified MAGs that belong to the same taxon with different colonization properties, highlighting the importance of an appropriate level of resolution to explore the functional basis of colonization and to identify targets for cultivation, hypothesis generation, and testing in model systems.

CONCLUSIONS: The analytical strategy adopted in our study can provide genomic insights into bacterial populations that may be critical to the efficacy of FMT due to their success in gut colonization and metabolic properties, and guide cultivation efforts to investigate mechanistic underpinnings of this procedure beyond associations.

METHODS: We used shotgun metagenomics together with assembly and binning strategies to reconstruct metagenome-assembled genomes (MAGs) from fecal samples of a single FMT donor. We then used metagenomic mapping to track the occurrence and distribution patterns of donor MAGs in two FMT recipients.

RESULTS: Our analyses revealed that 22% of the 92 highly complete bacterial MAGs that we identified from the donor successfully colonized and remained abundant in two recipients for at least 8 weeks. Most MAGs with a high colonization rate belonged to the order Bacteroidales. The vast majority of those that lacked evidence of colonization belonged to the order Clostridiales, and colonization success was negatively correlated with the number of genes related to sporulation. Our analysis of 151 publicly available gut metagenomes showed that the donor MAGs that colonized both recipients were prevalent, and the ones that colonized neither were rare across the participants of the Human Microbiome Project. Although our dataset showed a link between taxonomy and the colonization ability of a given MAG, we also identified MAGs that belong to the same taxon with different colonization properties, highlighting the importance of an appropriate level of resolution to explore the functional basis of colonization and to identify targets for cultivation, hypothesis generation, and testing in model systems.

CONCLUSIONS: The analytical strategy adopted in our study can provide genomic insights into bacterial populations that may be critical to the efficacy of FMT due to their success in gut colonization and metabolic properties, and guide cultivation efforts to investigate mechanistic underpinnings of this procedure beyond associations.},
}

A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome.

PeerJ, 5:e3254 pii:3254.

BACKGROUND: The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM).

METHODS: The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP).

RESULTS: Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus.

CONCLUSIONS: These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.

@article {pmid28462050,
year = {2017},
author = {Walsh, CJ and Guinane, CM and O' Toole, PW and Cotter, PD},
title = {A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome.},
journal = {PeerJ},
volume = {5},
number = {},
pages = {e3254},
doi = {10.7717/peerj.3254},
pmid = {28462050},
issn = {2167-8359},
abstract = {BACKGROUND: The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM).

METHODS: The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP).

RESULTS: Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus.

CONCLUSIONS: These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.},
}

RevDate: 2018-10-03CmpDate: 2017-06-27

Fisher CK, Mora T, AM Walczak (2017)

Variable habitat conditions drive species covariation in the human microbiota.

PLoS computational biology, 13(4):e1005435 pii:PCOMPBIOL-D-16-01259.

Two species with similar resource requirements respond in a characteristic way to variations in their habitat-their abundances rise and fall in concert. We use this idea to learn how bacterial populations in the microbiota respond to habitat conditions that vary from person-to-person across the human population. Our mathematical framework shows that habitat fluctuations are sufficient for explaining intra-bodysite correlations in relative species abundances from the Human Microbiome Project. We explicitly show that the relative abundances of closely related species are positively correlated and can be predicted from taxonomic relationships. We identify a small set of functional pathways related to metabolism and maintenance of the cell wall that form the basis of a common resource sharing niche space of the human microbiota.

@article {pmid28448493,
year = {2017},
author = {Fisher, CK and Mora, T and Walczak, AM},
title = {Variable habitat conditions drive species covariation in the human microbiota.},
journal = {PLoS computational biology},
volume = {13},
number = {4},
pages = {e1005435},
doi = {10.1371/journal.pcbi.1005435},
pmid = {28448493},
issn = {1553-7358},
mesh = {Computational Biology ; *Ecosystem ; Humans ; Microbiota/*genetics/*physiology ; Models, Biological ; Species Specificity ; },
abstract = {Two species with similar resource requirements respond in a characteristic way to variations in their habitat-their abundances rise and fall in concert. We use this idea to learn how bacterial populations in the microbiota respond to habitat conditions that vary from person-to-person across the human population. Our mathematical framework shows that habitat fluctuations are sufficient for explaining intra-bodysite correlations in relative species abundances from the Human Microbiome Project. We explicitly show that the relative abundances of closely related species are positively correlated and can be predicted from taxonomic relationships. We identify a small set of functional pathways related to metabolism and maintenance of the cell wall that form the basis of a common resource sharing niche space of the human microbiota.},
}

The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.

@article {pmid28437450,
year = {2017},
author = {Cai, Y and Zheng, W and Yao, J and Yang, Y and Mai, V and Mao, Q and Sun, Y},
title = {ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time.},
journal = {PLoS computational biology},
volume = {13},
number = {4},
pages = {e1005518},
doi = {10.1371/journal.pcbi.1005518},
pmid = {28437450},
issn = {1553-7358},
support = {R01 AI125982/AI/NIAID NIH HHS/United States ; },
mesh = {*Algorithms ; *Cluster Analysis ; Computational Biology ; Databases, Genetic ; Humans ; Microbiota/genetics ; RNA, Ribosomal, 16S/genetics ; Sequence Alignment/*methods ; Sequence Analysis, RNA/*methods ; },
abstract = {The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.},
}

Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

@article {pmid28435844,
year = {2016},
author = {Stanley, N and Shai, S and Taylor, D and Mucha, PJ},
title = {Clustering network layers with the strata multilayer stochastic block model.},
journal = {IEEE transactions on network science and engineering},
volume = {3},
number = {2},
pages = {95-105},
doi = {10.1109/TNSE.2016.2537545},
pmid = {28435844},
issn = {2327-4697},
support = {R01 HD075712/HD/NICHD NIH HHS/United States ; },
abstract = {Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.},
}

The Human Microbiome Project was first established to understand the roles of human-associated microbes to human health and disease. This study presents preliminary findings of Thai female facial skin microbiome using three pooled samples from groups of skin microbiome profiles, namely (1) healthy and (2) acne-prone young adults (teenage.hea and teenage.acn) and (3) healthy elderly adults (elderly.hea) based on standard dermatological criteria. These samples were sequenced using 454-pyrosequencing targeting 16S rRNA (V3-V4 regions). Good's coverage index of greater than 92% shows sufficient sampling of our data for each group. Three unique OTUs for each microbiome profile (43, 258 and 59 for teenage.hea, teenage.acn and ederly.hea, respectively) were obtained with 134 shared OTUs among the three datasets. Based on Morisita-Horn similarity coefficient, age is the major factor that brings the community relationship factor closer. The comparison among the three datasets reveal majority of Gemmatimonadetes, Planctomycetes and Nitrospirae in the teenage.hea, whereas Firmicutes are more prevalent in teenage.acn and elderly.hea skin types. In addition, when comparing Thai facial microbial diversity with the 16S data from U.S. forehead female database, significant differences were found among orders of bacteria, pointing to possible differences in human ecto-flora.

Salivary microbiome of an urban Indian cohort and patterns linked to subclinical inflammation.

Oral diseases, 23(7):926-940.

OBJECTIVE: To profile salivary microbiomes of an urban-living, healthy Indian cohort and explore associations with proinflammatory status.

METHODS: Fifty-one clinically healthy Indian subjects' salivary microbiomes were analyzed using 16S rRNA Illumina MiSeq sequencing. Community distribution was compared with salivary data from the Human Microbiome Project (HMP). Indian subjects were clustered using microbiome-based "partitioning along medoids" (PAM), and relationships of interleukin-1 beta levels with community composition were analyzed.

CONCLUSION: The salivary microbiome of this urban-dwelling Indian cohort differed significantly from that of a well-studied Western cohort. Specific community patterns were putatively associated with subclinical inflammation levels.

METHODS: Fifty-one clinically healthy Indian subjects' salivary microbiomes were analyzed using 16S rRNA Illumina MiSeq sequencing. Community distribution was compared with salivary data from the Human Microbiome Project (HMP). Indian subjects were clustered using microbiome-based "partitioning along medoids" (PAM), and relationships of interleukin-1 beta levels with community composition were analyzed.

CONCLUSION: The salivary microbiome of this urban-dwelling Indian cohort differed significantly from that of a well-studied Western cohort. Specific community patterns were putatively associated with subclinical inflammation levels.},
}

Hot Topics in Primary Care: Role of the Microbiome in Disease: Implications for Treatment of Irritable Bowel Syndrome.

The Journal of family practice, 66(4 Suppl):S40-S45.

Dietary and some other treatments for IBS are supported by a growing body of evidence, much of which comes from programs such as the Human Microbiome Project and Human Gut Microbiome Initiative, which were intended to identify and characterize microorganisms found in association with both healthy and diseased humans. These programs used state-of-the-art technology to characterize the human microbiome from multiple body sites. This evidence indicates that the gut microbiome plays an important role in IBS and some other gastrointestinal (GI) disorders.

@article {pmid28375407,
year = {2017},
author = {Lacy, BE},
title = {Hot Topics in Primary Care: Role of the Microbiome in Disease: Implications for Treatment of Irritable Bowel Syndrome.},
journal = {The Journal of family practice},
volume = {66},
number = {4 Suppl},
pages = {S40-S45},
pmid = {28375407},
issn = {1533-7294},
mesh = {Anti-Bacterial Agents/*therapeutic use ; Education, Medical, Continuing ; Gastrointestinal Microbiome/*drug effects ; Humans ; Irritable Bowel Syndrome/*diagnosis/*drug therapy/microbiology/physiopathology ; *Practice Guidelines as Topic ; Primary Health Care/*standards ; Probiotics/*therapeutic use ; United States ; },
abstract = {Dietary and some other treatments for IBS are supported by a growing body of evidence, much of which comes from programs such as the Human Microbiome Project and Human Gut Microbiome Initiative, which were intended to identify and characterize microorganisms found in association with both healthy and diseased humans. These programs used state-of-the-art technology to characterize the human microbiome from multiple body sites. This evidence indicates that the gut microbiome plays an important role in IBS and some other gastrointestinal (GI) disorders.},
}

Until the middle of the 20th century, clinical microbiology was limited to bacterial cultures enabling the detection of pathogenic microorganisms. Knowledge about the mutual relationship between humans and microorganisms has increased slowly. With the introduction of culture-independent analysis methods, comprehensive cataloging of the human microbiome was possible for the first time. Since then, compositional changes in relation to diseases have been studied. The goals of the Human Microbiome Project and MetaHIT include comparative studies of healthy and diseased individuals. Numerous libraries on time- and location-dependent changes of the microbiota composition in human diseases have been created. However, a mathematical correlation does not equal biological or medical relevance. Future research needs to validate the hypotheses generated in these studies in functional experiments and evaluate their true impact on clinical practice.

@article {pmid28357466,
year = {2017},
author = {Steinhagen, PR and Baumgart, DC},
title = {[Fundamentals of the microbiome].},
journal = {Der Internist},
volume = {58},
number = {5},
pages = {429-434},
doi = {10.1007/s00108-017-0224-1},
pmid = {28357466},
issn = {1432-1289},
mesh = {Disease ; Humans ; *Microbial Consortia ; },
abstract = {Until the middle of the 20th century, clinical microbiology was limited to bacterial cultures enabling the detection of pathogenic microorganisms. Knowledge about the mutual relationship between humans and microorganisms has increased slowly. With the introduction of culture-independent analysis methods, comprehensive cataloging of the human microbiome was possible for the first time. Since then, compositional changes in relation to diseases have been studied. The goals of the Human Microbiome Project and MetaHIT include comparative studies of healthy and diseased individuals. Numerous libraries on time- and location-dependent changes of the microbiota composition in human diseases have been created. However, a mathematical correlation does not equal biological or medical relevance. Future research needs to validate the hypotheses generated in these studies in functional experiments and evaluate their true impact on clinical practice.},
}

The Extreme Microbiome Project (XMP) is a project launched by the Association of Biomolecular Resource Facilities Metagenomics Research Group (ABRF MGRG) that focuses on whole genome shotgun sequencing of extreme and unique environments using a wide variety of biomolecular techniques. The goals are multifaceted, including development and refinement of new techniques for the following: 1) the detection and characterization of novel microbes, 2) the evaluation of nucleic acid techniques for extremophilic samples, and 3) the identification and implementation of the appropriate bioinformatics pipelines. Here, we highlight the different ongoing projects that we have been working on, as well as details on the various methods we use to characterize the microbiome and metagenome of these complex samples. In particular, we present data of a novel multienzyme extraction protocol that we developed, called Polyzyme or MetaPolyZyme. Presently, the XMP is characterizing sample sites around the world with the intent of discovering new species, genes, and gene clusters. Once a project site is complete, the resulting data will be publically available. Sites include Lake Hillier in Western Australia, the "Door to Hell" crater in Turkmenistan, deep ocean brine lakes of the Gulf of Mexico, deep ocean sediments from Greenland, permafrost tunnels in Alaska, ancient microbial biofilms from Antarctica, Blue Lagoon Iceland, Ethiopian toxic hot springs, and the acidic hypersaline ponds in Western Australia.

@article {pmid28337070,
year = {2017},
author = {Tighe, S and Afshinnekoo, E and Rock, TM and McGrath, K and Alexander, N and McIntyre, A and Ahsanuddin, S and Bezdan, D and Green, SJ and Joye, S and Stewart Johnson, S and Baldwin, DA and Bivens, N and Ajami, N and Carmical, JR and Herriott, IC and Colwell, R and Donia, M and Foox, J and Greenfield, N and Hunter, T and Hoffman, J and Hyman, J and Jorgensen, E and Krawczyk, D and Lee, J and Levy, S and Garcia-Reyero, N and Settles, M and Thomas, K and Gómez, F and Schriml, L and Kyrpides, N and Zaikova, E and Penterman, J and Mason, CE},
title = {Genomic Methods and Microbiological Technologies for Profiling Novel and Extreme Environments for the Extreme Microbiome Project (XMP).},
journal = {Journal of biomolecular techniques : JBT},
volume = {28},
number = {1},
pages = {31-39},
doi = {10.7171/jbt.17-2801-004},
pmid = {28337070},
issn = {1943-4731},
support = {R01 AI125416/AI/NIAID NIH HHS/United States ; R01 ES021006/ES/NIEHS NIH HHS/United States ; R01 NS076465/NS/NINDS NIH HHS/United States ; R25 EB020393/EB/NIBIB NIH HHS/United States ; },
mesh = {DNA, Bacterial/genetics/isolation & purification ; *Environmental Microbiology ; Extreme Environments ; Metagenome ; Microbiota/*genetics ; Molecular Typing/standards ; RNA, Bacterial/genetics/isolation & purification ; Reference Standards ; Sequence Analysis, DNA/standards ; },
abstract = {The Extreme Microbiome Project (XMP) is a project launched by the Association of Biomolecular Resource Facilities Metagenomics Research Group (ABRF MGRG) that focuses on whole genome shotgun sequencing of extreme and unique environments using a wide variety of biomolecular techniques. The goals are multifaceted, including development and refinement of new techniques for the following: 1) the detection and characterization of novel microbes, 2) the evaluation of nucleic acid techniques for extremophilic samples, and 3) the identification and implementation of the appropriate bioinformatics pipelines. Here, we highlight the different ongoing projects that we have been working on, as well as details on the various methods we use to characterize the microbiome and metagenome of these complex samples. In particular, we present data of a novel multienzyme extraction protocol that we developed, called Polyzyme or MetaPolyZyme. Presently, the XMP is characterizing sample sites around the world with the intent of discovering new species, genes, and gene clusters. Once a project site is complete, the resulting data will be publically available. Sites include Lake Hillier in Western Australia, the "Door to Hell" crater in Turkmenistan, deep ocean brine lakes of the Gulf of Mexico, deep ocean sediments from Greenland, permafrost tunnels in Alaska, ancient microbial biofilms from Antarctica, Blue Lagoon Iceland, Ethiopian toxic hot springs, and the acidic hypersaline ponds in Western Australia.},
}

The proliferation and intensification of diseases have forced every researcher to take actions for a robust understanding of the organisms. This demands deep knowledge about the cells and tissues in an organ and its entire surroundings, more precisely the microbiome community which involves viruses, bacteria, archaea, among others. They play an important role in the function of our body, and act both as a deterrent as well as shelter for diseases. Therefore, it is pertinent to study the relation within the microbiome in a human body. In this work, we analyze the sequence data provided through the Human Microbiome Project to explore evolutionary relations within blood microbiome. The objective is to analyze the common proteins present in the different microbes in the blood and find their phylogeny. The analysis of the phylogenetic relation between these species provides important insights about the conservedness of phylogeny of blood microbiome. Interestingly, the co-existence of five of those common proteins is observed in human too.

@article {pmid28216014,
year = {2017},
author = {Bhattacharyya, M and Ghosh, T and Shankar, S and Tomar, N},
title = {The conserved phylogeny of blood microbiome.},
journal = {Molecular phylogenetics and evolution},
volume = {109},
number = {},
pages = {404-408},
doi = {10.1016/j.ympev.2017.02.001},
pmid = {28216014},
issn = {1095-9513},
mesh = {Archaea/classification ; Bacteria/*classification ; Bacterial Proteins/genetics ; *Biological Evolution ; Blood/*microbiology ; Fungal Proteins/genetics ; Humans ; *Microbiota ; Phylogeny ; },
abstract = {The proliferation and intensification of diseases have forced every researcher to take actions for a robust understanding of the organisms. This demands deep knowledge about the cells and tissues in an organ and its entire surroundings, more precisely the microbiome community which involves viruses, bacteria, archaea, among others. They play an important role in the function of our body, and act both as a deterrent as well as shelter for diseases. Therefore, it is pertinent to study the relation within the microbiome in a human body. In this work, we analyze the sequence data provided through the Human Microbiome Project to explore evolutionary relations within blood microbiome. The objective is to analyze the common proteins present in the different microbes in the blood and find their phylogeny. The analysis of the phylogenetic relation between these species provides important insights about the conservedness of phylogeny of blood microbiome. Interestingly, the co-existence of five of those common proteins is observed in human too.},
}

An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

BMC bioinformatics, 18(1):94 pii:10.1186/s12859-017-1516-0.

BACKGROUND: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.

RESULTS: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.

CONCLUSIONS: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

RESULTS: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.

CONCLUSIONS: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.},
}

BACKGROUND: Recently, a complex microbiome was comprehensibly characterized in the serum and ascitic fluid of cirrhotic patients. In the current study, we investigated for the first time the induction of inflammatory pathways and Nitric Oxide, as well as the systemic hemodynamics in conjunction with the blood microbiome in a Child-Pugh class B cirrhotic cohort.

METHODS AND FINDINGS: We used the Intestinal Infections Microbial DNA qPCR Array to screen for 53 bacterial DNA from the gut in the blood. Assays were designed using the 16S rRNA gene as a target, and PCR amplification primers (based on the Human Microbiome Project) and hydrolysis-probe detection. Eighteen systemic hemodynamic parameters were measured non-invasively by impedance cardiography using the BioZ ICG monitor. The inflammatory response was assessed by measuring blood cytokines, Nitric Oxide RNA arrays, and Nitric Oxide. In the blood of this cirrhotic cohort, we detected 19 of 53 bacterial species tested. The number of bacterial species was markedly increased in the blood of cirrhotic patients compared to control individuals (0.2+/-0.4 vs 3.1+/-2.3; 95% CI: 1.3 to 4.9; P = 0.0030). The total bacterial DNA was also increased in the blood of cirrhotic subjects compared to control subjects (0.2+/- 1.1 vs 41.8+/-132.1; 95% CI: 6.0 to 77.2; P = 0.0022). In the cirrhotic cohort, the Cardiac Output increased by 37% and the Systemic Vascular Resistance decreased by 40% (P< 0.00001 for both compared to control subjects). Systemic Vascular Resistance was inversely correlated to blood bacterial DNA quantity (- 0.621; 95% CI -0.843 to -0.218; P = 0.0060), blood bacterial species number (- 0.593; 95% CI -0.83 to -0.175; P = 0.0095; logistic regression: Chi Square = 5.8877; P = 0.0152), and serum Nitric Oxide (- 0.705; 95% CI -0.881 to -0.355; P = 0.0011). Many members of the Nitric Oxide signaling pathway gene family were increased in cirrhotic subjects.

CONCLUSIONS: Our study identified blood bacterial DNA in ~ 90% of the cirrhotic patients without clinical evidences of infection, and suggests that the quantity of bacterial DNA in blood may stimulate signaling pathways, including Nitric Oxide, that could decrease systemic vascular resistance and increase cardiac output.

METHODS AND FINDINGS: We used the Intestinal Infections Microbial DNA qPCR Array to screen for 53 bacterial DNA from the gut in the blood. Assays were designed using the 16S rRNA gene as a target, and PCR amplification primers (based on the Human Microbiome Project) and hydrolysis-probe detection. Eighteen systemic hemodynamic parameters were measured non-invasively by impedance cardiography using the BioZ ICG monitor. The inflammatory response was assessed by measuring blood cytokines, Nitric Oxide RNA arrays, and Nitric Oxide. In the blood of this cirrhotic cohort, we detected 19 of 53 bacterial species tested. The number of bacterial species was markedly increased in the blood of cirrhotic patients compared to control individuals (0.2+/-0.4 vs 3.1+/-2.3; 95% CI: 1.3 to 4.9; P = 0.0030). The total bacterial DNA was also increased in the blood of cirrhotic subjects compared to control subjects (0.2+/- 1.1 vs 41.8+/-132.1; 95% CI: 6.0 to 77.2; P = 0.0022). In the cirrhotic cohort, the Cardiac Output increased by 37% and the Systemic Vascular Resistance decreased by 40% (P< 0.00001 for both compared to control subjects). Systemic Vascular Resistance was inversely correlated to blood bacterial DNA quantity (- 0.621; 95% CI -0.843 to -0.218; P = 0.0060), blood bacterial species number (- 0.593; 95% CI -0.83 to -0.175; P = 0.0095; logistic regression: Chi Square = 5.8877; P = 0.0152), and serum Nitric Oxide (- 0.705; 95% CI -0.881 to -0.355; P = 0.0011). Many members of the Nitric Oxide signaling pathway gene family were increased in cirrhotic subjects.

CONCLUSIONS: Our study identified blood bacterial DNA in ~ 90% of the cirrhotic patients without clinical evidences of infection, and suggests that the quantity of bacterial DNA in blood may stimulate signaling pathways, including Nitric Oxide, that could decrease systemic vascular resistance and increase cardiac output.},
}

The gut microbiota modulate host biology in numerous ways, but little is known about the molecular mediators of these interactions. Previously, we found a widely distributed family of nonribosomal peptide synthetase gene clusters in gut bacteria. Here, by expressing a subset of these clusters in Escherichia coli or Bacillus subtilis, we show that they encode pyrazinones and dihydropyrazinones. At least one of the 47 clusters is present in 88% of the National Institutes of Health Human Microbiome Project (NIH HMP) stool samples, and they are transcribed under conditions of host colonization. We present evidence that the active form of these molecules is the initially released peptide aldehyde, which bears potent protease inhibitory activity and selectively targets a subset of cathepsins in human cell proteomes. Our findings show that an approach combining bioinformatics, synthetic biology, and heterologous gene cluster expression can rapidly expand our knowledge of the metabolic potential of the microbiota while avoiding the challenges of cultivating fastidious commensals.

High-throughput sequencing of small-subunit (SSU) rRNA genes has revolutionized understanding of microbial communities and facilitated investigations into ecological dynamics at unprecedented scales. Such extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain a substantial proportion of unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. Indeed, these novel taxonomic lineages are associated with so-called microbial "dark matter," which is the genomic potential of these lineages. Unfortunately, characterization beyond "unclassified" is challenging due to relatively short read lengths and large data set sizes. Here we demonstrate how mining of phylogenetically novel sequences from microbial ecosystems can be automated using SSUnique, a software pipeline that filters unclassified and/or rare operational taxonomic units (OTUs) from 16S rRNA gene sequence libraries by screening against consensus structural models for SSU rRNA. Phylogenetic position is inferred against a reference data set, and additional characterization of novel clades is also included, such as targeted probe/primer design and mining of assembled metagenomes for genomic context. We show how SSUnique reproduced a previous analysis of phylogenetic novelty from an Arctic tundra soil and demonstrate the recovery of highly novel clades from data sets associated with both the Earth Microbiome Project (EMP) and Human Microbiome Project (HMP). We anticipate that SSUnique will add to the expanding computational toolbox supporting high-throughput sequencing approaches for the study of microbial ecology and phylogeny. IMPORTANCE Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.

@article {pmid28028549,
year = {2016},
author = {Lynch, MD and Neufeld, JD},
title = {SSUnique: Detecting Sequence Novelty in Microbiome Surveys.},
journal = {mSystems},
volume = {1},
number = {6},
pages = {},
doi = {10.1128/mSystems.00133-16},
pmid = {28028549},
issn = {2379-5077},
abstract = {High-throughput sequencing of small-subunit (SSU) rRNA genes has revolutionized understanding of microbial communities and facilitated investigations into ecological dynamics at unprecedented scales. Such extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain a substantial proportion of unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. Indeed, these novel taxonomic lineages are associated with so-called microbial "dark matter," which is the genomic potential of these lineages. Unfortunately, characterization beyond "unclassified" is challenging due to relatively short read lengths and large data set sizes. Here we demonstrate how mining of phylogenetically novel sequences from microbial ecosystems can be automated using SSUnique, a software pipeline that filters unclassified and/or rare operational taxonomic units (OTUs) from 16S rRNA gene sequence libraries by screening against consensus structural models for SSU rRNA. Phylogenetic position is inferred against a reference data set, and additional characterization of novel clades is also included, such as targeted probe/primer design and mining of assembled metagenomes for genomic context. We show how SSUnique reproduced a previous analysis of phylogenetic novelty from an Arctic tundra soil and demonstrate the recovery of highly novel clades from data sets associated with both the Earth Microbiome Project (EMP) and Human Microbiome Project (HMP). We anticipate that SSUnique will add to the expanding computational toolbox supporting high-throughput sequencing approaches for the study of microbial ecology and phylogeny. IMPORTANCE Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.},
}

RevDate: 2018-05-29CmpDate: 2018-05-29

Jackrel SL, Owens SM, Gilbert JA, et al (2017)

Identifying the plant-associated microbiome across aquatic and terrestrial environments: the effects of amplification method on taxa discovery.

Molecular ecology resources, 17(5):931-942.

Plants in terrestrial and aquatic environments contain a diverse microbiome. Yet, the chloroplast and mitochondria organelles of the plant eukaryotic cell originate from free-living cyanobacteria and Rickettsiales. This represents a challenge for sequencing the plant microbiome with universal primers, as ~99% of 16S rRNA sequences may consist of chloroplast and mitochondrial sequences. Peptide nucleic acid clamps offer a potential solution by blocking amplification of host-associated sequences. We assessed the efficacy of chloroplast and mitochondria-blocking clamps against a range of microbial taxa from soil, freshwater and marine environments. While we found that the mitochondrial blocking clamps appear to be a robust method for assessing animal-associated microbiota, Proteobacterial 16S rRNA binds to the chloroplast-blocking clamp, resulting in a strong sequencing bias against this group. We attribute this bias to a conserved 14-bp sequence in the Proteobacteria that matches the 17-bp chloroplast-blocking clamp sequence. By scanning the Greengenes database, we provide a reference list of nearly 1500 taxa that contain this 14-bp sequence, including 48 families such as the Rhodobacteraceae, Phyllobacteriaceae, Rhizobiaceae, Kiloniellaceae and Caulobacteraceae. To determine where these taxa are found in nature, we mapped this taxa reference list against the Earth Microbiome Project database. These taxa are abundant in a variety of environments, particularly aquatic and semiaquatic freshwater and marine habitats. To facilitate informed decisions on effective use of organelle-blocking clamps, we provide a searchable database of microbial taxa in the Greengenes and Silva databases matching various n-mer oligonucleotides of each PNA sequence.

@article {pmid27997751,
year = {2017},
author = {Jackrel, SL and Owens, SM and Gilbert, JA and Pfister, CA},
title = {Identifying the plant-associated microbiome across aquatic and terrestrial environments: the effects of amplification method on taxa discovery.},
journal = {Molecular ecology resources},
volume = {17},
number = {5},
pages = {931-942},
doi = {10.1111/1755-0998.12645},
pmid = {27997751},
issn = {1755-0998},
mesh = {Bacteria/*classification/*genetics ; DNA, Bacterial/chemistry/genetics ; DNA, Ribosomal/chemistry/genetics ; Metagenomics/*methods ; *Microbiota ; Plants/*microbiology ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; },
abstract = {Plants in terrestrial and aquatic environments contain a diverse microbiome. Yet, the chloroplast and mitochondria organelles of the plant eukaryotic cell originate from free-living cyanobacteria and Rickettsiales. This represents a challenge for sequencing the plant microbiome with universal primers, as ~99% of 16S rRNA sequences may consist of chloroplast and mitochondrial sequences. Peptide nucleic acid clamps offer a potential solution by blocking amplification of host-associated sequences. We assessed the efficacy of chloroplast and mitochondria-blocking clamps against a range of microbial taxa from soil, freshwater and marine environments. While we found that the mitochondrial blocking clamps appear to be a robust method for assessing animal-associated microbiota, Proteobacterial 16S rRNA binds to the chloroplast-blocking clamp, resulting in a strong sequencing bias against this group. We attribute this bias to a conserved 14-bp sequence in the Proteobacteria that matches the 17-bp chloroplast-blocking clamp sequence. By scanning the Greengenes database, we provide a reference list of nearly 1500 taxa that contain this 14-bp sequence, including 48 families such as the Rhodobacteraceae, Phyllobacteriaceae, Rhizobiaceae, Kiloniellaceae and Caulobacteraceae. To determine where these taxa are found in nature, we mapped this taxa reference list against the Earth Microbiome Project database. These taxa are abundant in a variety of environments, particularly aquatic and semiaquatic freshwater and marine habitats. To facilitate informed decisions on effective use of organelle-blocking clamps, we provide a searchable database of microbial taxa in the Greengenes and Silva databases matching various n-mer oligonucleotides of each PNA sequence.},
}

Effects of combined oral contraceptives, depot medroxyprogesterone acetate and the levonorgestrel-releasing intrauterine system on the vaginal microbiome.

Contraception, 95(4):405-413.

OBJECTIVES: Prior studies suggest that the composition of the vaginal microbiome may positively or negatively affect susceptibility to sexually transmitted infections (STIs) and bacterial vaginosis (BV). Some female hormonal contraceptive methods also appear to positively or negatively influence STI transmission and BV. Therefore, changes in the vaginal microbiome that are associated with different contraceptive methods may explain, in part, effects on STI transmission and BV.

STUDY DESIGN: We performed a retrospective study of 16S rRNA gene survey data of vaginal samples from a subset of participants from the Human Vaginal Microbiome Project at Virginia Commonwealth University. The subset included 682 women who reported using a single form of birth control that was condoms, combined oral contraceptives (COCs), depot medroxyprogesterone acetate (DMPA) or the levonorgestrel-releasing intrauterine system (LNG-IUS).

STUDY DESIGN: We performed a retrospective study of 16S rRNA gene survey data of vaginal samples from a subset of participants from the Human Vaginal Microbiome Project at Virginia Commonwealth University. The subset included 682 women who reported using a single form of birth control that was condoms, combined oral contraceptives (COCs), depot medroxyprogesterone acetate (DMPA) or the levonorgestrel-releasing intrauterine system (LNG-IUS).

Sequencing and bioinformatics technologies have advanced rapidly in recent years, driven largely by developments in next-generation sequencing (NGS) technology. Given the increasing importance of these advances, there is a growing need to incorporate concepts and practices relating to NGS into undergraduate and high school science curricula. We believe that direct access to sequencing and bioinformatics will improve the ability of students to understand the information obtained through these increasingly ubiquitous research tools. In this commentary, we discuss approaches and challenges for bringing NGS into the classroom based on our experiences in developing and running a microbiome project in high school and undergraduate courses. We describe strategies for maximizing student engagement through establishing personal relevance and utilizing an inquiry-based structure. Additionally, we address the practical issues of incorporating cutting edge technologies into an established curriculum. Looking forward, we anticipate that NGS educational experiments will become more commonplace as sequencing costs continue to decrease and the workflow becomes more user friendly.

@article {pmid27856569,
year = {2016},
author = {Hartman, MR and Harrington, KT and Etson, CM and Fierman, MB and Slonim, DK and Walt, DR},
title = {Personal microbiomes and next-generation sequencing for laboratory-based education.},
journal = {FEMS microbiology letters},
volume = {363},
number = {23},
pages = {},
doi = {10.1093/femsle/fnw266},
pmid = {27856569},
issn = {1574-6968},
support = {K12 GM074869/GM/NIGMS NIH HHS/United States ; R25 OD010547/OD/NIH HHS/United States ; },
mesh = {Computational Biology/*education ; *Curriculum ; High-Throughput Nucleotide Sequencing/*methods ; Humans ; Microbiota/*genetics ; Schools ; Students ; },
abstract = {Sequencing and bioinformatics technologies have advanced rapidly in recent years, driven largely by developments in next-generation sequencing (NGS) technology. Given the increasing importance of these advances, there is a growing need to incorporate concepts and practices relating to NGS into undergraduate and high school science curricula. We believe that direct access to sequencing and bioinformatics will improve the ability of students to understand the information obtained through these increasingly ubiquitous research tools. In this commentary, we discuss approaches and challenges for bringing NGS into the classroom based on our experiences in developing and running a microbiome project in high school and undergraduate courses. We describe strategies for maximizing student engagement through establishing personal relevance and utilizing an inquiry-based structure. Additionally, we address the practical issues of incorporating cutting edge technologies into an established curriculum. Looking forward, we anticipate that NGS educational experiments will become more commonplace as sequencing costs continue to decrease and the workflow becomes more user friendly.},
}

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

@article {pmid27822515,
year = {2016},
author = {Kopylova, E and Navas-Molina, JA and Mercier, C and Xu, ZZ and Mahé, F and He, Y and Zhou, HW and Rognes, T and Caporaso, JG and Knight, R},
title = {Open-Source Sequence Clustering Methods Improve the State Of the Art.},
journal = {mSystems},
volume = {1},
number = {1},
pages = {},
doi = {10.1128/mSystems.00003-15},
pmid = {27822515},
issn = {2379-5077},
abstract = {Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).},
}

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RJR Experience and Expertise

Researcher

Robbins holds BS, MS, and PhD degrees in the life sciences. He served
as a tenured faculty member in the Zoology and Biological Science
departments at Michigan State University. He is currently exploring
the intersection between genomics, microbial ecology, and biodiversity
— an area that promises to transform our understanding of the
biosphere.

Educator

Robbins has extensive experience in college-level education: At MSU he
taught introductory biology, genetics, and population genetics. At
JHU, he was an instructor for a special course on biological database
design. At FHCRC, he team-taught a graduate-level course on the
history of genetics. At Bellevue College he taught medical
informatics.

Administrator

Robbins has been involved in science administration at both the
federal and the institutional levels. At NSF he was a program officer
for database activities in the life sciences, at DOE he was a program
officer for information infrastructure in the human genome project. At
the Fred Hutchinson Cancer Research Center, he served as a vice
president for fifteen years.

Technologist

Robbins has been involved with information technology since writing
his first Fortran program as a college student. At NSF he was the first
program officer for database activities in the life sciences. At JHU
he held an appointment in the CS department and served as director of
the informatics core for the Genome Data Base. At the FHCRC he was VP
for Information Technology.

Publisher

While still at Michigan State, Robbins started his first publishing
venture, founding a small company that addressed the short-run
publishing needs of instructors in very large undergraduate classes.
For more than 20 years, Robbins has been operating The Electronic Scholarly Publishing Project,
a web site dedicated to the digital publishing of critical works in
science, especially classical genetics.

Speaker

Robbins is well-known for his speaking abilities and is often called
upon to provide keynote or plenary addresses at international
meetings. For example, in July, 2012, he gave a well-received keynote address at the
Global Biodiversity Informatics Congress, sponsored by GBIF and held
in Copenhagen. The slides from that talk can be seen
HERE.

Facilitator

Robbins is a skilled meeting facilitator.
He prefers a participatory approach, with
part of the meeting involving dynamic breakout groups, created by the
participants in real time: (1) individuals propose breakout groups;
(2) everyone signs up for one (or more) groups; (3) the groups
with the most interested parties then meet, with reports from each
group presented and discussed in a subsequent plenary session.

Designer

Robbins has been engaged with photography and design since the 1960s,
when he worked for a professional photography laboratory. He now
prefers digital photography and tools for their precision and
reproducibility. He designed his first web site more than 20 years
ago and he personally designed and implemented this web site.
He engages in graphic design as a hobby.

For most of human existence, microbes were hidden, visible only through
the illnesses they caused. When they finally surfaced in biological
studies, they were cast as rogues. Only recently have they immigrated
from the neglected fringes of biology to its center. Even today, many
people think of microbes as germs to be eradicated, but those that live
with us — the microbiome — are invaluable parts of our lives.
I Contain Multitudes lets us peer into that world for the first
time, allowing us to see how ubiquitous and vital microbes are: they
sculpt our organs, defend us from disease, break down our food, educate
our immune systems, guide our behavior, bombard our genomes with their
genes, and grant us incredible abilities.

Reprints and preprints of publications, slide presentations,
instructional materials, and data compilations written or
prepared by Robert Robbins. Most papers deal with
computational biology, genome informatics, using information
technology to support biomedical research, and related matters.

ResearchGate is a social networking site for scientists and
researchers to share papers, ask and answer questions, and
find collaborators. According to a study by
Nature
and an
article in
Times Higher Education
, it is the largest academic
social network in terms of active users.